Text Analysis - Sound Harmony

Author

Leona Hoxha

Project Introduction

Abstract

How difficult and tiring has it been the last time you wanted to invest in a good pair of headphones? Headphones have become an essential part of our daily lives, whether we’re listening to music, watching videos, or making calls, and choosing the perfect headphones can feel tricky with so many options available.

In this project, I analyzed how people rated eleven specific headphones through reviews and comments on Reddit platform. Moreover, since many headphone brand websites lack reviewer sections, Reddit offers a platform for users to share genuine experiences and opinions. Given that, I want to see if the reviews/comments from Reddit align with the article rating for those specific headphones. Additionally, I want to see which rating from these two has a stronger link with the technical specifications of the headphones, and which rating is more reliable for the potential customers.

As for the sentiment analysis, I will be analyzing three different models, and look deeply into which model is performing best for this project’s topic. I will also do LDA modelling to check which topics are mosly discussed when it comes to the headphones I chose. Overall, this project shows the importance of balancing technical specifications with user experiences when choosing headphones. By adding real user feedback, individuals can make better informed decisions for their needs.

Introduction

In today’s world, we see new headphones hitting the market all the time. With so many brands and features to choose from, it’s not easy to find the headphones that are just right. Some people might just look at the price and hope for the best, but a higher price doesn’t always mean better sound or quality.

On the other hand, some people turn to online articles to find the trendiest headphones. While these articles can be helpful, they might not always reflect what actual users think or feel about those headphones.

That’s why I’ve decided to take a closer look at reviews and comments from Reddit for eleven different headphones. Alongside this, I’ll also be analyzing articles from various online sources to see how they rate these headphones.

DATA

For this project, I will use 2 different data sets. The first one is the data set which consists of 11 headphones found from different websites/articles and the second data set has the reddit reviews of each headphone. Some headphones have more reviews such as around 500 reviews, whereas some have around 90 reviews, where I still hope to achieve a good sentiment analysis for those products by doing weighted formula on the results.

Headphone_ArticleList Data set

From the articles I have been able to gather technical data for eleven headphones, where eight are ranked as “good” according to the articles and three of them are ranked as “not good” headphones. The reason why I got “good” and “bad” headphones, is because I wanted to have a more balanced dataset which consisted of different rated headphones, brands, types, connectivity and so on. Furthermore, six of them are from articles published in 2024, four of them are from 2023 and one of the headphones in the list is from the article published in 2022. I was only able to gather the data manually since the websites of the headphone brands don’t allow to scrape the data.

Description of the variables in this data set:

Field	Description
Headphone Name	The specific name of the headphone model.
Brand	The brand name of the headphone.
Article/Website	The name of the article or website that published the headphone.
Year	The publication year of the article source.
Type	The headphone design type: over-ear, in-ear, or on-ear.
Connectivity	The connectivity options of the headphone: wireless or hybrid (both wired and wireless).
Battery Life (hours)	The duration the headphone battery can last on a single charge, measured in hours.
Battery Capacity with Case (mAh)	The battery capacity of the headphone and its charging case, measured in milliampere-hours (mAh).
Active Noise Cancellation	Indicates if the headphone features active noise cancellation technology.
Bluetooth Version	The version of Bluetooth technology supported by the headphone.
Driver Sensitivity (dB)	The sensitivity of the headphone’s driver, measured in decibels (dB).
Driver Size (mm)	The diameter size of the headphone driver, measured in millimeters (mm).
Tag/Comment	Additional labels or comments provided by the article or publication about the headphone.
Article Rate	The rating or evaluation given to the headphone by the article or publication (good or not good).

Initially, I got the Price of each headphone as well, but since the prices vary between different websites and geographical locations, I removed it. I also removed the Frequency Response since almost all of the headphones were in the range of 20Hz~40KHz (wouldn’t be a helpful variable).

RedditComments Data Set

For this data set I have been able to gather in total 6100 reviews/comments for the 11 headphones, by using the help of Reddit Api. Initially, I had more reviews/comments, but some posts were not related to the headphones, due to the fact that reddit also shows posts that have a similar word in the search query, therefore I removed those posts manually, and it resulted in removing around 500 hundred reviews/comments. I have gathered some reviews in the r/headphones community(subreddit), since it has 1.1 Milion members and that seems like the right place to find some decent discussions around the headphones. However, for some of the headphones in my data set, there wasn’t much discussions in r/headphones community, therefore I tried to gather some comments/reviews using the “all of Reddit” (different subreddit - not a specific one) in the search bar.

Description of the variables in this data set:

Field	Description
Headphone Name	The specific name of the headphone model.
Subreddit	The name of the subreddit where the post was published.
Post_Title	The title of the post.
Post_ID	The unique identifier for the post.
Post_Author	The username of the author who created the post.
Post_URL	The URL link to the post.
Post_Score	The net upvotes received by the post, indicating its popularity among users.
Num_Comments	The total number of comments on the post.
Created	The Coordinated Universal Time (UTC) timestamp when the post was created on Reddit.
Comment	The comment or review related to the headphone in the specific post.
Comment_Author	The username of the author who posted the comment.
Comment_Score	The net upvotes received by the comment, indicating its popularity among users.

Importing Libraries

In this section, I’m importing the necessary libraries for the entire project, organizing them based on their respective applications. The libraries have been categorized and ordered according to their specific utility in the project.

Code

import pandas as pd                   # For data manipulation and analysis
import re                             # For regular expressions
import os                             # For interacting with the operating system
import numpy as np                    # For numerical computing
import matplotlib.pyplot as plt       # For data visualization
import seaborn as sns                 # For enhanced data visualization
from wordcloud import WordCloud       # For generating word clouds
from sklearn.feature_extraction.text import CountVectorizer  # For text preprocessing
from sklearn.decomposition import LatentDirichletAllocation  # For topic modeling
from scipy.special import softmax     # For softmax function
from transformers import AutoTokenizer, AutoModelForSequenceClassification  # For sentiment analysis using ROBERTA model
import warnings                       # For ignoring warnings
from tqdm import tqdm                 # For displaying progress bars

Importing the Data

Code

headphones = pd.read_csv('Headphones_ArticleList.csv', sep=",", header=0, index_col=False)

reddit = pd.read_csv('RedditComments.csv', sep=",", header=0, index_col=False)

Code

# Displaying information about the datasets
print("Information about the Headphone_ArticleList Data set:")
print(headphones.info())

print("Information about the RedditComments Data Set:")
print(reddit.info())

Information about the Headphone_ArticleList Data set:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11 entries, 0 to 10
Data columns (total 14 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   Headphone Name                    11 non-null     object 
 1   Brand                             11 non-null     object 
 2   Article/Website                   11 non-null     object 
 3   Year                              11 non-null     int64  
 4   Type                              11 non-null     object 
 5   Connectivity                      11 non-null     object 
 6   Battery Life (hours)              11 non-null     int64  
 7   Battery Capacity with Case (mAh)  11 non-null     int64  
 8   Active Noise Cancellation         11 non-null     object 
 9   Bluetooth Version                 11 non-null     float64
 10  Driver Sensitivity (dB)           11 non-null     int64  
 11  Driver Size (mm)                  11 non-null     float64
 12  Tag/Comment                       11 non-null     object 
 13  Article Rate                      11 non-null     object 
dtypes: float64(2), int64(4), object(8)
memory usage: 1.3+ KB
None
Information about the RedditComments Data Set:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6100 entries, 0 to 6099
Data columns (total 12 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Headphone Name  6100 non-null   object
 1   Subreddit       6100 non-null   object
 2   Post_Title      6100 non-null   object
 3   Post_ID         6100 non-null   object
 4   Post_Author     6100 non-null   object
 5   Post_URL        6100 non-null   object
 6   Post_Score      6100 non-null   int64 
 7   Num_Comments    6100 non-null   int64 
 8   Created         6100 non-null   int64 
 9   Comment         6100 non-null   object
 10  Comment_Author  6100 non-null   object
 11  Comment_Score   6100 non-null   int64 
dtypes: int64(4), object(8)
memory usage: 572.0+ KB
None

PREPROCESSING STEPS

Data Transformation

Code

# Convert 'Article Rate' to binary column
headphones['Article Rate'] = headphones['Article Rate'].map({'good': 1, 'not good': 0})

# Convert 'Active Noise Cancellation' to binary column
headphones['Active Noise Cancellation'] = headphones['Active Noise Cancellation'].map({'yes': 1, 'no': 0})

# Convert 'Connectivity' to binary column
headphones['Connectivity'] = headphones['Connectivity'].map({'both': 1, 'wireless': 0})

Data Cleaning

Code

# Define a function to clean the text
def clean_text(text):
    # Remove URLs
    text = re.sub(r'http\S+|www\S+|https\S+', '', text, flags=re.MULTILINE)
    
    # Remove special characters and numbers
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    
    # Remove extra spaces
    text = re.sub(r'\s+', ' ', text).strip()
    
    return text

# Apply the clean_text function to the 'Comment' column in the reddit DataFrame
reddit['Comment'] = reddit['Comment'].apply(clean_text)

DATA VISUALIZATION

Visualization of Headphone Data Set

I defined a custom function, plot_count_distribution_with_annotations, to plot the count distribution of categorical variables. I apply this function to visualize the distribution of ‘Article Rate’, ‘Brand’, ‘Type’, and ‘Connectivity’ within the dataset, providing insights into the frequency of each category. Additionally, I defined another function, plot_correlation_matrix, to visualize the correlation matrix of numerical features in the dataset, which shows the relationships between them.

Code

# Set the aesthetic style of the plots
sns.set(style="whitegrid")

# Define a function to plot count distribution with annotations
def plot_count_distribution_with_annotations(data, x, title, figsize=(10, 6)):
    plt.figure(figsize=figsize)
    ax = sns.countplot(data=data, x=x, palette="Set2", order=data[x].value_counts().index)
    plt.title(title, fontsize=16, weight='bold')
    plt.xlabel('')
    plt.ylabel('Count', fontsize=14)
    
    # Annotate each bar with the count value
    for p in ax.patches:
        ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()), 
                    ha='center', va='baseline', fontsize=12, color='black')
    
    plt.xticks(rotation=45)
    plt.show()

# Define a function to plot correlation matrix
def plot_correlation_matrix(data, title, figsize=(12, 8)):
    plt.figure(figsize=figsize)
    sns.heatmap(data.corr(), annot=True, cmap='viridis', fmt='.2f', linewidths=1, linecolor='black')
    plt.title(title, fontsize=16, weight='bold')
    plt.show()

# Visualize the distribution of 'Article Rate' with annotations
plot_count_distribution_with_annotations(headphones, 'Article Rate', 'Distribution of Article Rate')

# Visualizing 'Brand' distribution with annotations
plot_count_distribution_with_annotations(headphones, 'Brand', 'Distribution of Headphone Brands', figsize=(14, 8))

# Visualizing 'Type' distribution with annotations
plot_count_distribution_with_annotations(headphones, 'Type', 'Distribution of Headphone Types')

# Visualizing 'Connectivity' distribution with annotations
plot_count_distribution_with_annotations(headphones, 'Connectivity', 'Distribution of Headphone Connectivity')

# Plotting correlation matrix for numerical features
plot_correlation_matrix(headphones, 'Correlation Matrix for Headphone Features')

C:\Users\ADMIN\AppData\Local\Temp\ipykernel_4592\4186866323.py:23: FutureWarning:

The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.

The bar visualizations show the information about the headphones I have in the headphones data set, in terms of the how many headphones are rate as “good” or “not good”, the distribution of brands in the data set, how many of them are in-ear, over-ear and on-ear, and how many of them are wireless or have hybrid connectivity. I wanted my data set to not be biased in terms of only “good” headphones. That’s why I chose some of them that were rated as “bad” headphones in the articles. Initially, I had over 50 headphones in my data set, but it was very difficult to find all the necessary technical data on the internet for all of them (since every headphone brand puts the information differently on their official websites). Therefore, there were too many missing values to keep all of the headphones, that’s why I chose to stick with all these 11 headphones, where eight of them are rated as “good” and three of them are rated as “not good”. As for connectivity, four of them are hybrid (meaning wireless and wired) and seven of them are wireless. From the correlation matrix visualization, I can see that there’s some correlation between Article Rate and the other variables in the headphones data set. For example, the variables Connectivity, Driver Sensitivity and Driver Size are moderately correlated with Article Rate, whereas the other variables are weakly correlated with it. I can also see that Driver Size is strongly correlated with Connectivity, Battery Life and Battery Capacity.

Text Length Analysis

Here, I create a new feature, Comment_Length, in the reddit data set to represent the length (the number of characters) of each comment across different headphones.

Next, I define the function plot_text_length_histogram, to plot histograms for the comment length distribution. Each subplot represents a unique headphone, and its color is determined using the ‘husl’ color palette from Seaborn, ensuring distinct colors for each headphone.

Code

# Create a new feature 'Comment_Length'
reddit['Comment_Length'] = reddit['Comment'].apply(len)

# Text Length Analysis

# Define a function to plot histograms for text length
def plot_text_length_histogram(data, title, color, ax):
    sns.histplot(data=data, x='Comment_Length', kde=True, ax=ax, color=color)
    ax.set_title(title, fontsize=16)
    ax.set_xlabel('Comment Length', fontsize=14)
    ax.set_ylabel('Frequency', fontsize=14)

# Create a 3x4 grid for subplots
fig, axs = plt.subplots(4, 3, figsize=(20, 15))

# Flatten the axs array for easy iteration
axs = axs.flatten()

# Get unique headphone names
headphones_names= reddit['Headphone Name'].unique()

# Loop through each headphone to plot its comment length distribution
for i, headphone in enumerate(headphones_names):
    subset = reddit[reddit['Headphone Name'] == headphone]
    color = sns.color_palette("husl", len(headphones_names))[i]
    title = f'Comment Length Distribution for {headphone}'
    plot_text_length_histogram(subset, title, color, axs[i])

# Hide unused subplots
for j in range(len(headphones_names), 12):
    axs[j].axis('off')

plt.tight_layout()
plt.show()

From this visualization, I can see that none of the headphones has the same comment length distribution as the others. Some of them have a lot more comments than others by just looking at the histogram bin widths and the frequency limits on the y-axis, for example, samsung galaxy buds 2 pro and sony wh-1000xm5 have a lot more comments than sony wh-ch520 and jbl endurance peak ii. Due to this fact, the further analysis with these headphones could be considered as “biased” if I don’t properly interpret the sentiments results by using the weighted formula to calculate the overall score.

Word Cloud

For this part, I visualize the most frequent words present in the Reddit comments using a word cloud. A word cloud provides a visual representation of text data, where the size of each word indicates its frequency in the text (Wordcloud: What’s it all about?, 2023).

First, I define the function generate_word_cloud, to generate a word cloud from the “Comments” variable. The resulting word cloud is saved as ‘word_cloud.png’ then loaded and displayed as below:

Code

def generate_word_cloud(data, save_path=None):
    """
    Generate a word cloud from the provided text data.
    
    Parameters:
    - data: String containing the text data to generate the word cloud from
    - save_path: Optional path to save the word cloud image
    
    Returns:
    None
    """
    # Generate a word cloud for the comments
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(data)

    # Display the word cloud
    plt.figure(figsize=(10, 5))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    plt.title('Word Cloud for Reddit Comments')
    
    # Save the word cloud to an image file if save_path is provided
    if save_path:
        plt.savefig(save_path, bbox_inches='tight')
        print(f"Word cloud saved to {save_path}")
    else:
        plt.show()

text = ' '.join(reddit['Comment'])
save_path = 'word_cloud.png'

# Check if the word cloud image already exists
if os.path.exists(save_path):
    # Load and display the saved word cloud image
    img = plt.imread(save_path)
    plt.figure(figsize=(10, 5))
    plt.imshow(img)
    plt.axis('off')
    plt.title('Word Cloud for Reddit Comments (Saved)')
    plt.show()
else:
    # Generate and save the word cloud
    generate_word_cloud(text, save_path)

From this visualization, I can see that the most terms used in the comments are Sound Quality, Ear, Buds, ANC, Audio, Music, Bass, Battery, Mic and Bluetooth which all are related to this topic. Additionally, I can also see some of the headphone brands, which had the highest number of comments as I stated before, such as Samsung Galaxy Buds and Airpods.

Topic Modeling using Latent Dirichlet Allocation (LDA)

Now, I perform topic modeling on the Reddit comments using Latent Dirichlet Allocation (LDA). Topic modeling is a technique used to discover topics present in a collection of documents/comments. LDA is a probabilistic model that assumes each document is a mixture of a small number of topics, and each word in the document is attributable to one of the document’s topics (Kulshrestha, 2019).

I set the maximum word frequency (max_df) to 0.95 (95%) and the minimum word frequency (min_df) to 2 to filter out words that are too frequent or too rare. Additionally, I remove English stop words to focus on meaningful words. I specify n_components=5 to indicate that I want to identify 5 topics in the dataset.
Then I define the function display_topics to display the top words associated with each topic. This function sorts the words in each topic by their contribution to the topic and displays the top no_top_words words for each topic.

Lastly I group the comments by ‘Headphone Name’ and perform LDA topic modeling separately for each group. This allows me to identify and display the dominant topics specific to each headphone brand.

Code

warnings.filterwarnings('ignore')

# Vectorize the text data
vectorizer = CountVectorizer(max_df=0.95, min_df=2, stop_words='english')
data_vectorized = vectorizer.fit_transform(reddit['Comment'])

# Apply LDA
lda_model = LatentDirichletAllocation(n_components=5, random_state=0)
lda_model.fit(data_vectorized)

# Get the dominant topic for each comment
def get_dominant_topic(lda_model, data_vectorized):
    dominant_topics = []
    for i in range(data_vectorized.shape[0]):  # Use shape[0] to get the number of rows
        topic_weights = lda_model.transform(data_vectorized[i])
        dominant_topic = np.argmax(topic_weights)
        dominant_topics.append(dominant_topic)
    return dominant_topics

reddit['Dominant_Topic'] = get_dominant_topic(lda_model, data_vectorized)

# Define a function to display topics for each headphone
def display_topics(model, feature_names, no_top_words):
    topics = {}
    for topic_idx, topic in enumerate(model.components_):
        topics["Topic {}".format(topic_idx+1)] = [feature_names[i] for i in topic.argsort()[:-no_top_words - 1:-1]]
    return topics

no_top_words = 5
feature_names = vectorizer.get_feature_names_out()

# Group by 'Headphone Name' and display topics
for name, group in reddit.groupby('Headphone Name'):
    print(f"Topics for {name}:")
    data_vectorized_group = vectorizer.transform(group['Comment'])
    lda_model_group = LatentDirichletAllocation(n_components=5, random_state=0)
    lda_model_group.fit(data_vectorized_group)
    topics = display_topics(lda_model_group, feature_names, no_top_words)
    for topic, terms in topics.items():
        print(f"{topic}: {' '.join(terms)}")
    print("="*50)

Topics for airpods pro 2nd generation:
Topic 1: like sound apple airpods quality
Topic 2: anc gen sound noise airpods
Topic 3: airpods real got apple pro
Topic 4: sound real refurbished like pair
Topic 5: good sound better just airpods
==================================================
Topics for edifier stax spirit s3:
Topic 1: headphones like sound wireless stax
Topic 2: stax ms really think wireless
Topic 3: audeze products bit price look
Topic 4: closed lcd mobius audeze like
Topic 5: stax edifier audeze sound headphones
==================================================
Topics for jbl endurance peak ii:
Topic 1: jbl thank doesnt tried ive
Topic 2: thanks solution works alot entering
Topic 3: worked case right bluetooth times
Topic 4: thanks worked im good right
Topic 5: thank gave lol works like
==================================================
Topics for oneplus buds pro 2:
Topic 1: buds pro sound oneplus good
Topic 2: im tips foam noise using
Topic 3: oneplus pro buds earbuds phone
Topic 4: buds pro battery great life
Topic 5: sound ive did noise just
==================================================
Topics for samsung galaxy buds 2 pro:
Topic 1: sound quality buds headphones like
Topic 2: ear noise like buds airpods
Topic 3: buds ear just ive im
Topic 4: buds deleted pro apple galaxy
Topic 5: good sound bass consumer just
==================================================
Topics for samsung galaxy buds fe:
Topic 1: buds sound like fe fit
Topic 2: buds just tips anc pair
Topic 3: buds ear use pro just
Topic 4: buds fe pro fit galaxy
Topic 5: good touch yes foam samsung
==================================================
Topics for sony wf-1000xm5:
Topic 1: xm like im sound dont
Topic 2: anc noise xms ears just
Topic 3: pro ear im better buds
Topic 4: years great xms better sound
Topic 5: xm xms just sound bass
==================================================
Topics for sony wh-1000xm5:
Topic 1: headphones sound xm like im
Topic 2: xms max airpods xm sony
Topic 3: got use time yes definitely
Topic 4: welcome deleted xms youre dont
Topic 5: xm sound xms anc better
==================================================
Topics for sony wh-ch520:
Topic 1: like sound headphones battery im
Topic 2: thanks headphones ear bot automatically
Topic 3: ear pads im working replacement
Topic 4: im ch sidetone headphones like
Topic 5: good quite ive theyre clear
==================================================
Topics for soundcore space q45:
Topic 1: battery headphones sound bass quality
Topic 2: listening qc battery issues happened
Topic 3: send just volume space got
Topic 4: wired just qs headphones batteries
Topic 5: battery did good brand batteries
==================================================
Topics for steelseries arctis nova pro:
Topic 1: sound like pro im headset
Topic 2: mic just headset like ear
Topic 3: wireless just pro headset nova
Topic 4: gaming sound wireless guide headsets
Topic 5: good fulla impressed offer yesterday
==================================================

From this visualization I can look into the most common discussions or interests across all the headphones. As for example for the sony wh-ch520, I can see the word “clear” therefore I might suppose that they have clear sounds, the word “replacement” together with “pads” can mean that the ear pads may need replacement after a while. For samsung galaxy buds 2 pro, “good sound bass” can mean that they have a good bass. For jbl endurance peak ii, I can see a lot of “thanks, worked” which can mean that a lot of the comments were about the issues customers were having and they were trying to get some help at reddit. For soundcore space q45, I can see a lot of “good battery” which can mean that the battery capacity/life is good from the experience of the commenters. Moreover, for the steelseries arctis nove pro, “gaming” can mean that these headphones are recommended for gaming enthusiasts.

SENTIMENT ANALYSIS

ROBERTA MODEL

Initializing ROBERTA Model

As a first step, I’m initializing a pre-trained sentiment analysis model called ‘cardiffnlp/twitter-roberta-base-sentiment’. This is a Roberta-base model which is trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark and is suitable for English (Francesco Barbieri, 2020). Then, I’m using the Hugging Face Transformers library to load both the tokenizer and the model for sequence classification from this pre-trained model.

Code

MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)

Sentiment Analysis using ROBERTA Model

As a second step, I’ve defined a function named polarity_scores_roberta to perform sentiment analysis using the ROBERTA model. The function takes a comment as input, preprocesses it to fit the model’s token limit, and then calculates sentiment scores for negative, neutral, and positive sentiments. These scores are returned as a dictionary. Consecuently, I’m calculating sentiment scores for each comment using this function.

Code

def polarity_scores_roberta(example, max_length=512):
    # Truncate or pad the comment to the maximum token limit
    example = example[:max_length-2]  # -2 to account for [CLS] and [SEP] tokens
    
    encoded_text = tokenizer(example, return_tensors='pt', truncation=True, max_length=max_length)
    output = model(**encoded_text)
    scores = output.logits[0].detach().numpy()
    scores = softmax(scores)
    scores_dict = {
        'roberta_neg': scores[0],
        'roberta_neu': scores[1],
        'roberta_pos': scores[2]
    }
    return scores_dict

# Calculate sentiment scores for each comment using tqdm for loading visualization
sentiment_scores = []

# Use tqdm to display a progress bar
for comment in tqdm(reddit['Comment'], desc='Calculating sentiment scores'):
    scores = polarity_scores_roberta(comment)
    sentiment_scores.append(scores)

Calculating sentiment scores:   0%|          | 0/6100 [00:00<?, ?it/s]Calculating sentiment scores:   0%|          | 1/6100 [00:01<3:15:54,  1.93s/it]Calculating sentiment scores:   0%|          | 3/6100 [00:02<55:34,  1.83it/s]  Calculating sentiment scores:   0%|          | 5/6100 [00:02<29:52,  3.40it/s]Calculating sentiment scores:   0%|          | 7/6100 [00:02<21:03,  4.82it/s]Calculating sentiment scores:   0%|          | 9/6100 [00:02<15:13,  6.67it/s]Calculating sentiment scores:   0%|          | 11/6100 [00:02<11:57,  8.49it/s]Calculating sentiment scores:   0%|          | 13/6100 [00:02<12:00,  8.44it/s]Calculating sentiment scores:   0%|          | 15/6100 [00:02<10:11,  9.95it/s]Calculating sentiment scores:   0%|          | 17/6100 [00:03<09:03, 11.18it/s]Calculating sentiment scores:   0%|          | 19/6100 [00:03<09:58, 10.17it/s]Calculating sentiment scores:   0%|          | 21/6100 [00:03<10:31,  9.62it/s]Calculating sentiment scores:   0%|          | 24/6100 [00:03<08:17, 12.21it/s]Calculating sentiment scores:   0%|          | 26/6100 [00:03<07:55, 12.76it/s]Calculating sentiment scores:   0%|          | 28/6100 [00:04<08:55, 11.35it/s]Calculating sentiment scores:   0%|          | 30/6100 [00:04<08:22, 12.07it/s]Calculating sentiment scores:   1%|          | 32/6100 [00:04<08:24, 12.03it/s]Calculating sentiment scores:   1%|          | 34/6100 [00:04<07:33, 13.37it/s]Calculating sentiment scores:   1%|          | 36/6100 [00:04<06:59, 14.45it/s]Calculating sentiment scores:   1%|          | 38/6100 [00:04<07:04, 14.27it/s]Calculating sentiment scores:   1%|          | 40/6100 [00:04<06:50, 14.74it/s]Calculating sentiment scores:   1%|          | 42/6100 [00:05<07:12, 14.01it/s]Calculating sentiment scores:   1%|          | 44/6100 [00:05<08:07, 12.43it/s]Calculating sentiment scores:   1%|          | 46/6100 [00:05<07:53, 12.78it/s]Calculating sentiment scores:   1%|          | 48/6100 [00:05<08:03, 12.51it/s]Calculating sentiment scores:   1%|          | 50/6100 [00:05<08:42, 11.57it/s]Calculating sentiment scores:   1%|          | 52/6100 [00:05<08:30, 11.84it/s]Calculating sentiment scores:   1%|          | 54/6100 [00:06<08:55, 11.29it/s]Calculating sentiment scores:   1%|          | 56/6100 [00:06<09:44, 10.34it/s]Calculating sentiment scores:   1%|          | 58/6100 [00:06<09:45, 10.33it/s]Calculating sentiment scores:   1%|          | 60/6100 [00:06<08:20, 12.06it/s]Calculating sentiment scores:   1%|          | 62/6100 [00:06<09:12, 10.94it/s]Calculating sentiment scores:   1%|          | 64/6100 [00:07<09:20, 10.76it/s]Calculating sentiment scores:   1%|          | 66/6100 [00:07<08:31, 11.79it/s]Calculating sentiment scores:   1%|          | 68/6100 [00:07<07:47, 12.90it/s]Calculating sentiment scores:   1%|          | 70/6100 [00:07<07:34, 13.26it/s]Calculating sentiment scores:   1%|          | 72/6100 [00:07<07:49, 12.83it/s]Calculating sentiment scores:   1%|          | 74/6100 [00:07<08:06, 12.38it/s]Calculating sentiment scores:   1%|          | 76/6100 [00:07<07:55, 12.66it/s]Calculating sentiment scores:   1%|▏         | 78/6100 [00:08<07:13, 13.91it/s]Calculating sentiment scores:   1%|▏         | 80/6100 [00:08<07:02, 14.24it/s]Calculating sentiment scores:   1%|▏         | 82/6100 [00:08<06:46, 14.80it/s]Calculating sentiment scores:   1%|▏         | 84/6100 [00:08<06:58, 14.39it/s]Calculating sentiment scores:   1%|▏         | 86/6100 [00:08<07:04, 14.16it/s]Calculating sentiment scores:   1%|▏         | 88/6100 [00:08<07:03, 14.18it/s]Calculating sentiment scores:   1%|▏         | 90/6100 [00:08<07:05, 14.13it/s]Calculating sentiment scores:   2%|▏         | 92/6100 [00:08<06:47, 14.74it/s]Calculating sentiment scores:   2%|▏         | 94/6100 [00:09<07:57, 12.57it/s]Calculating sentiment scores:   2%|▏         | 96/6100 [00:09<08:05, 12.37it/s]Calculating sentiment scores:   2%|▏         | 98/6100 [00:09<08:27, 11.84it/s]Calculating sentiment scores:   2%|▏         | 100/6100 [00:09<07:34, 13.20it/s]Calculating sentiment scores:   2%|▏         | 102/6100 [00:09<07:29, 13.33it/s]Calculating sentiment scores:   2%|▏         | 104/6100 [00:09<07:19, 13.65it/s]Calculating sentiment scores:   2%|▏         | 106/6100 [00:10<06:56, 14.39it/s]Calculating sentiment scores:   2%|▏         | 108/6100 [00:10<07:03, 14.14it/s]Calculating sentiment scores:   2%|▏         | 110/6100 [00:10<06:43, 14.84it/s]Calculating sentiment scores:   2%|▏         | 112/6100 [00:10<06:18, 15.83it/s]Calculating sentiment scores:   2%|▏         | 114/6100 [00:10<06:23, 15.61it/s]Calculating sentiment scores:   2%|▏         | 116/6100 [00:10<07:17, 13.68it/s]Calculating sentiment scores:   2%|▏         | 118/6100 [00:10<06:46, 14.72it/s]Calculating sentiment scores:   2%|▏         | 120/6100 [00:10<06:32, 15.22it/s]Calculating sentiment scores:   2%|▏         | 122/6100 [00:11<07:45, 12.84it/s]Calculating sentiment scores:   2%|▏         | 124/6100 [00:11<07:01, 14.19it/s]Calculating sentiment scores:   2%|▏         | 126/6100 [00:11<06:34, 15.14it/s]Calculating sentiment scores:   2%|▏         | 128/6100 [00:11<06:14, 15.96it/s]Calculating sentiment scores:   2%|▏         | 131/6100 [00:11<05:44, 17.30it/s]Calculating sentiment scores:   2%|▏         | 133/6100 [00:11<05:48, 17.15it/s]Calculating sentiment scores:   2%|▏         | 135/6100 [00:11<05:59, 16.60it/s]Calculating sentiment scores:   2%|▏         | 137/6100 [00:12<06:41, 14.85it/s]Calculating sentiment scores:   2%|▏         | 139/6100 [00:12<06:31, 15.22it/s]Calculating sentiment scores:   2%|▏         | 141/6100 [00:12<06:54, 14.37it/s]Calculating sentiment scores:   2%|▏         | 143/6100 [00:12<07:09, 13.87it/s]Calculating sentiment scores:   2%|▏         | 145/6100 [00:12<06:58, 14.24it/s]Calculating sentiment scores:   2%|▏         | 147/6100 [00:12<08:13, 12.06it/s]Calculating sentiment scores:   2%|▏         | 149/6100 [00:13<08:55, 11.12it/s]Calculating sentiment scores:   2%|▏         | 151/6100 [00:13<08:05, 12.24it/s]Calculating sentiment scores:   3%|▎         | 154/6100 [00:13<06:50, 14.49it/s]Calculating sentiment scores:   3%|▎         | 156/6100 [00:13<06:34, 15.07it/s]Calculating sentiment scores:   3%|▎         | 158/6100 [00:13<07:12, 13.73it/s]Calculating sentiment scores:   3%|▎         | 161/6100 [00:13<06:20, 15.63it/s]Calculating sentiment scores:   3%|▎         | 163/6100 [00:13<06:56, 14.26it/s]Calculating sentiment scores:   3%|▎         | 165/6100 [00:14<08:25, 11.74it/s]Calculating sentiment scores:   3%|▎         | 167/6100 [00:14<08:05, 12.21it/s]Calculating sentiment scores:   3%|▎         | 169/6100 [00:14<07:24, 13.34it/s]Calculating sentiment scores:   3%|▎         | 171/6100 [00:14<07:00, 14.10it/s]Calculating sentiment scores:   3%|▎         | 173/6100 [00:14<07:32, 13.10it/s]Calculating sentiment scores:   3%|▎         | 175/6100 [00:14<07:20, 13.46it/s]Calculating sentiment scores:   3%|▎         | 177/6100 [00:15<07:25, 13.31it/s]Calculating sentiment scores:   3%|▎         | 179/6100 [00:15<06:41, 14.75it/s]Calculating sentiment scores:   3%|▎         | 182/6100 [00:15<06:55, 14.25it/s]Calculating sentiment scores:   3%|▎         | 184/6100 [00:15<08:39, 11.38it/s]Calculating sentiment scores:   3%|▎         | 186/6100 [00:15<07:52, 12.52it/s]Calculating sentiment scores:   3%|▎         | 189/6100 [00:15<06:45, 14.59it/s]Calculating sentiment scores:   3%|▎         | 191/6100 [00:16<07:33, 13.02it/s]Calculating sentiment scores:   3%|▎         | 193/6100 [00:16<07:42, 12.76it/s]Calculating sentiment scores:   3%|▎         | 195/6100 [00:16<08:29, 11.59it/s]Calculating sentiment scores:   3%|▎         | 198/6100 [00:16<07:11, 13.68it/s]Calculating sentiment scores:   3%|▎         | 200/6100 [00:16<07:52, 12.48it/s]Calculating sentiment scores:   3%|▎         | 202/6100 [00:17<07:22, 13.33it/s]Calculating sentiment scores:   3%|▎         | 204/6100 [00:17<07:07, 13.78it/s]Calculating sentiment scores:   3%|▎         | 206/6100 [00:17<07:12, 13.64it/s]Calculating sentiment scores:   3%|▎         | 209/6100 [00:17<06:24, 15.31it/s]Calculating sentiment scores:   3%|▎         | 211/6100 [00:17<06:02, 16.26it/s]Calculating sentiment scores:   3%|▎         | 213/6100 [00:17<05:56, 16.50it/s]Calculating sentiment scores:   4%|▎         | 215/6100 [00:17<05:46, 16.99it/s]Calculating sentiment scores:   4%|▎         | 217/6100 [00:17<05:39, 17.35it/s]Calculating sentiment scores:   4%|▎         | 219/6100 [00:18<06:27, 15.16it/s]Calculating sentiment scores:   4%|▎         | 221/6100 [00:18<06:04, 16.13it/s]Calculating sentiment scores:   4%|▎         | 223/6100 [00:18<08:01, 12.22it/s]Calculating sentiment scores:   4%|▎         | 225/6100 [00:18<08:08, 12.02it/s]Calculating sentiment scores:   4%|▎         | 227/6100 [00:18<08:14, 11.89it/s]Calculating sentiment scores:   4%|▍         | 229/6100 [00:18<08:21, 11.72it/s]Calculating sentiment scores:   4%|▍         | 231/6100 [00:19<07:53, 12.40it/s]Calculating sentiment scores:   4%|▍         | 233/6100 [00:19<07:44, 12.62it/s]Calculating sentiment scores:   4%|▍         | 235/6100 [00:19<07:48, 12.53it/s]Calculating sentiment scores:   4%|▍         | 237/6100 [00:19<07:13, 13.53it/s]Calculating sentiment scores:   4%|▍         | 239/6100 [00:19<06:40, 14.64it/s]Calculating sentiment scores:   4%|▍         | 241/6100 [00:19<06:14, 15.67it/s]Calculating sentiment scores:   4%|▍         | 243/6100 [00:19<07:06, 13.72it/s]Calculating sentiment scores:   4%|▍         | 245/6100 [00:20<06:48, 14.32it/s]Calculating sentiment scores:   4%|▍         | 247/6100 [00:20<06:15, 15.59it/s]Calculating sentiment scores:   4%|▍         | 249/6100 [00:20<05:54, 16.49it/s]Calculating sentiment scores:   4%|▍         | 251/6100 [00:20<05:49, 16.76it/s]Calculating sentiment scores:   4%|▍         | 253/6100 [00:20<05:34, 17.48it/s]Calculating sentiment scores:   4%|▍         | 255/6100 [00:20<05:23, 18.07it/s]Calculating sentiment scores:   4%|▍         | 257/6100 [00:20<05:30, 17.66it/s]Calculating sentiment scores:   4%|▍         | 260/6100 [00:20<05:21, 18.18it/s]Calculating sentiment scores:   4%|▍         | 262/6100 [00:21<06:39, 14.63it/s]Calculating sentiment scores:   4%|▍         | 264/6100 [00:21<06:23, 15.24it/s]Calculating sentiment scores:   4%|▍         | 266/6100 [00:21<06:11, 15.71it/s]Calculating sentiment scores:   4%|▍         | 268/6100 [00:21<07:52, 12.35it/s]Calculating sentiment scores:   4%|▍         | 270/6100 [00:21<08:00, 12.13it/s]Calculating sentiment scores:   4%|▍         | 272/6100 [00:21<07:51, 12.35it/s]Calculating sentiment scores:   4%|▍         | 274/6100 [00:22<08:01, 12.11it/s]Calculating sentiment scores:   5%|▍         | 276/6100 [00:22<08:19, 11.66it/s]Calculating sentiment scores:   5%|▍         | 278/6100 [00:22<07:56, 12.22it/s]Calculating sentiment scores:   5%|▍         | 280/6100 [00:22<07:27, 12.99it/s]Calculating sentiment scores:   5%|▍         | 282/6100 [00:22<07:13, 13.42it/s]Calculating sentiment scores:   5%|▍         | 284/6100 [00:22<06:57, 13.94it/s]Calculating sentiment scores:   5%|▍         | 286/6100 [00:22<07:23, 13.10it/s]Calculating sentiment scores:   5%|▍         | 288/6100 [00:23<06:55, 14.00it/s]Calculating sentiment scores:   5%|▍         | 290/6100 [00:23<06:52, 14.08it/s]Calculating sentiment scores:   5%|▍         | 292/6100 [00:23<06:39, 14.53it/s]Calculating sentiment scores:   5%|▍         | 294/6100 [00:23<06:19, 15.31it/s]Calculating sentiment scores:   5%|▍         | 296/6100 [00:23<07:24, 13.06it/s]Calculating sentiment scores:   5%|▍         | 298/6100 [00:23<07:22, 13.10it/s]Calculating sentiment scores:   5%|▍         | 300/6100 [00:23<06:50, 14.14it/s]Calculating sentiment scores:   5%|▍         | 302/6100 [00:24<07:19, 13.18it/s]Calculating sentiment scores:   5%|▍         | 304/6100 [00:24<08:08, 11.86it/s]Calculating sentiment scores:   5%|▌         | 306/6100 [00:24<09:21, 10.32it/s]Calculating sentiment scores:   5%|▌         | 308/6100 [00:24<09:46,  9.87it/s]Calculating sentiment scores:   5%|▌         | 310/6100 [00:24<09:30, 10.15it/s]Calculating sentiment scores:   5%|▌         | 312/6100 [00:25<09:41,  9.96it/s]Calculating sentiment scores:   5%|▌         | 314/6100 [00:25<08:49, 10.93it/s]Calculating sentiment scores:   5%|▌         | 316/6100 [00:25<08:46, 10.98it/s]Calculating sentiment scores:   5%|▌         | 318/6100 [00:25<10:12,  9.44it/s]Calculating sentiment scores:   5%|▌         | 320/6100 [00:25<09:06, 10.57it/s]Calculating sentiment scores:   5%|▌         | 322/6100 [00:26<08:41, 11.08it/s]Calculating sentiment scores:   5%|▌         | 324/6100 [00:26<08:43, 11.04it/s]Calculating sentiment scores:   5%|▌         | 326/6100 [00:26<09:14, 10.42it/s]Calculating sentiment scores:   5%|▌         | 328/6100 [00:26<10:35,  9.08it/s]Calculating sentiment scores:   5%|▌         | 330/6100 [00:26<09:35, 10.03it/s]Calculating sentiment scores:   5%|▌         | 332/6100 [00:27<10:59,  8.75it/s]Calculating sentiment scores:   5%|▌         | 333/6100 [00:27<11:16,  8.53it/s]Calculating sentiment scores:   5%|▌         | 334/6100 [00:27<11:23,  8.44it/s]Calculating sentiment scores:   6%|▌         | 336/6100 [00:27<11:40,  8.22it/s]Calculating sentiment scores:   6%|▌         | 338/6100 [00:27<12:03,  7.96it/s]Calculating sentiment scores:   6%|▌         | 340/6100 [00:28<12:13,  7.85it/s]Calculating sentiment scores:   6%|▌         | 341/6100 [00:28<14:03,  6.83it/s]Calculating sentiment scores:   6%|▌         | 343/6100 [00:28<12:03,  7.96it/s]Calculating sentiment scores:   6%|▌         | 345/6100 [00:28<10:19,  9.29it/s]Calculating sentiment scores:   6%|▌         | 346/6100 [00:28<11:18,  8.48it/s]Calculating sentiment scores:   6%|▌         | 348/6100 [00:29<10:15,  9.34it/s]Calculating sentiment scores:   6%|▌         | 350/6100 [00:29<09:32, 10.05it/s]Calculating sentiment scores:   6%|▌         | 352/6100 [00:29<10:13,  9.36it/s]Calculating sentiment scores:   6%|▌         | 354/6100 [00:29<10:00,  9.57it/s]Calculating sentiment scores:   6%|▌         | 356/6100 [00:29<09:16, 10.32it/s]Calculating sentiment scores:   6%|▌         | 358/6100 [00:30<12:01,  7.96it/s]Calculating sentiment scores:   6%|▌         | 360/6100 [00:30<10:33,  9.07it/s]Calculating sentiment scores:   6%|▌         | 362/6100 [00:30<09:53,  9.67it/s]Calculating sentiment scores:   6%|▌         | 364/6100 [00:30<11:31,  8.29it/s]Calculating sentiment scores:   6%|▌         | 366/6100 [00:31<10:06,  9.46it/s]Calculating sentiment scores:   6%|▌         | 368/6100 [00:31<09:34,  9.97it/s]Calculating sentiment scores:   6%|▌         | 370/6100 [00:31<10:25,  9.16it/s]Calculating sentiment scores:   6%|▌         | 371/6100 [00:31<11:01,  8.66it/s]Calculating sentiment scores:   6%|▌         | 373/6100 [00:31<09:29, 10.05it/s]Calculating sentiment scores:   6%|▌         | 375/6100 [00:32<10:51,  8.79it/s]Calculating sentiment scores:   6%|▌         | 376/6100 [00:32<10:36,  8.99it/s]Calculating sentiment scores:   6%|▌         | 377/6100 [00:32<12:00,  7.94it/s]Calculating sentiment scores:   6%|▌         | 378/6100 [00:32<13:25,  7.10it/s]Calculating sentiment scores:   6%|▌         | 379/6100 [00:32<15:35,  6.11it/s]Calculating sentiment scores:   6%|▌         | 381/6100 [00:32<12:07,  7.86it/s]Calculating sentiment scores:   6%|▋         | 382/6100 [00:33<11:38,  8.18it/s]Calculating sentiment scores:   6%|▋         | 384/6100 [00:33<10:21,  9.20it/s]Calculating sentiment scores:   6%|▋         | 386/6100 [00:33<10:39,  8.94it/s]Calculating sentiment scores:   6%|▋         | 388/6100 [00:33<11:47,  8.07it/s]Calculating sentiment scores:   6%|▋         | 389/6100 [00:33<13:30,  7.05it/s]Calculating sentiment scores:   6%|▋         | 391/6100 [00:34<12:19,  7.71it/s]Calculating sentiment scores:   6%|▋         | 393/6100 [00:34<13:37,  6.98it/s]Calculating sentiment scores:   6%|▋         | 394/6100 [00:34<15:17,  6.22it/s]Calculating sentiment scores:   6%|▋         | 396/6100 [00:35<14:52,  6.39it/s]Calculating sentiment scores:   7%|▋         | 397/6100 [00:35<13:55,  6.83it/s]Calculating sentiment scores:   7%|▋         | 398/6100 [00:35<15:46,  6.02it/s]Calculating sentiment scores:   7%|▋         | 399/6100 [00:35<17:04,  5.57it/s]Calculating sentiment scores:   7%|▋         | 401/6100 [00:35<15:41,  6.06it/s]Calculating sentiment scores:   7%|▋         | 402/6100 [00:36<14:55,  6.36it/s]Calculating sentiment scores:   7%|▋         | 403/6100 [00:36<15:31,  6.12it/s]Calculating sentiment scores:   7%|▋         | 405/6100 [00:36<12:39,  7.50it/s]Calculating sentiment scores:   7%|▋         | 406/6100 [00:36<14:12,  6.68it/s]Calculating sentiment scores:   7%|▋         | 407/6100 [00:36<15:36,  6.08it/s]Calculating sentiment scores:   7%|▋         | 408/6100 [00:36<14:16,  6.64it/s]Calculating sentiment scores:   7%|▋         | 410/6100 [00:37<11:21,  8.36it/s]Calculating sentiment scores:   7%|▋         | 411/6100 [00:37<13:34,  6.99it/s]Calculating sentiment scores:   7%|▋         | 412/6100 [00:37<15:24,  6.15it/s]Calculating sentiment scores:   7%|▋         | 413/6100 [00:37<16:25,  5.77it/s]Calculating sentiment scores:   7%|▋         | 414/6100 [00:37<17:04,  5.55it/s]Calculating sentiment scores:   7%|▋         | 416/6100 [00:38<12:37,  7.50it/s]Calculating sentiment scores:   7%|▋         | 418/6100 [00:38<12:38,  7.49it/s]Calculating sentiment scores:   7%|▋         | 419/6100 [00:38<12:32,  7.55it/s]Calculating sentiment scores:   7%|▋         | 421/6100 [00:38<11:05,  8.54it/s]Calculating sentiment scores:   7%|▋         | 423/6100 [00:38<10:42,  8.84it/s]Calculating sentiment scores:   7%|▋         | 424/6100 [00:39<12:17,  7.70it/s]Calculating sentiment scores:   7%|▋         | 425/6100 [00:39<13:35,  6.96it/s]Calculating sentiment scores:   7%|▋         | 426/6100 [00:39<14:45,  6.41it/s]Calculating sentiment scores:   7%|▋         | 428/6100 [00:39<12:51,  7.35it/s]Calculating sentiment scores:   7%|▋         | 429/6100 [00:39<12:20,  7.66it/s]Calculating sentiment scores:   7%|▋         | 430/6100 [00:39<13:42,  6.89it/s]Calculating sentiment scores:   7%|▋         | 431/6100 [00:40<13:50,  6.82it/s]Calculating sentiment scores:   7%|▋         | 432/6100 [00:40<12:43,  7.42it/s]Calculating sentiment scores:   7%|▋         | 434/6100 [00:40<10:06,  9.34it/s]Calculating sentiment scores:   7%|▋         | 435/6100 [00:40<10:50,  8.71it/s]Calculating sentiment scores:   7%|▋         | 436/6100 [00:40<13:01,  7.25it/s]Calculating sentiment scores:   7%|▋         | 438/6100 [00:40<10:17,  9.17it/s]Calculating sentiment scores:   7%|▋         | 440/6100 [00:40<08:49, 10.70it/s]Calculating sentiment scores:   7%|▋         | 442/6100 [00:41<09:42,  9.71it/s]Calculating sentiment scores:   7%|▋         | 444/6100 [00:41<10:00,  9.42it/s]Calculating sentiment scores:   7%|▋         | 446/6100 [00:41<08:46, 10.73it/s]Calculating sentiment scores:   7%|▋         | 448/6100 [00:41<09:18, 10.12it/s]Calculating sentiment scores:   7%|▋         | 450/6100 [00:42<10:14,  9.19it/s]Calculating sentiment scores:   7%|▋         | 452/6100 [00:42<08:39, 10.87it/s]Calculating sentiment scores:   7%|▋         | 454/6100 [00:42<07:44, 12.16it/s]Calculating sentiment scores:   7%|▋         | 456/6100 [00:42<08:27, 11.12it/s]Calculating sentiment scores:   8%|▊         | 458/6100 [00:42<12:19,  7.63it/s]Calculating sentiment scores:   8%|▊         | 460/6100 [00:43<12:23,  7.58it/s]Calculating sentiment scores:   8%|▊         | 462/6100 [00:43<10:38,  8.83it/s]Calculating sentiment scores:   8%|▊         | 464/6100 [00:43<09:07, 10.29it/s]Calculating sentiment scores:   8%|▊         | 466/6100 [00:43<09:15, 10.14it/s]Calculating sentiment scores:   8%|▊         | 468/6100 [00:43<10:19,  9.09it/s]Calculating sentiment scores:   8%|▊         | 470/6100 [00:44<09:31,  9.84it/s]Calculating sentiment scores:   8%|▊         | 472/6100 [00:44<09:01, 10.39it/s]Calculating sentiment scores:   8%|▊         | 474/6100 [00:44<08:28, 11.06it/s]Calculating sentiment scores:   8%|▊         | 476/6100 [00:44<08:40, 10.81it/s]Calculating sentiment scores:   8%|▊         | 478/6100 [00:44<09:05, 10.31it/s]Calculating sentiment scores:   8%|▊         | 480/6100 [00:44<08:22, 11.18it/s]Calculating sentiment scores:   8%|▊         | 482/6100 [00:45<08:24, 11.13it/s]Calculating sentiment scores:   8%|▊         | 484/6100 [00:45<08:32, 10.97it/s]Calculating sentiment scores:   8%|▊         | 486/6100 [00:45<07:45, 12.05it/s]Calculating sentiment scores:   8%|▊         | 488/6100 [00:45<07:34, 12.36it/s]Calculating sentiment scores:   8%|▊         | 490/6100 [00:45<07:36, 12.28it/s]Calculating sentiment scores:   8%|▊         | 492/6100 [00:45<06:57, 13.43it/s]Calculating sentiment scores:   8%|▊         | 494/6100 [00:46<06:32, 14.30it/s]Calculating sentiment scores:   8%|▊         | 496/6100 [00:46<07:06, 13.15it/s]Calculating sentiment scores:   8%|▊         | 498/6100 [00:46<06:28, 14.42it/s]Calculating sentiment scores:   8%|▊         | 500/6100 [00:46<06:04, 15.37it/s]Calculating sentiment scores:   8%|▊         | 502/6100 [00:46<06:43, 13.89it/s]Calculating sentiment scores:   8%|▊         | 504/6100 [00:46<07:54, 11.80it/s]Calculating sentiment scores:   8%|▊         | 506/6100 [00:47<08:00, 11.64it/s]Calculating sentiment scores:   8%|▊         | 508/6100 [00:47<07:51, 11.87it/s]Calculating sentiment scores:   8%|▊         | 510/6100 [00:47<07:33, 12.32it/s]Calculating sentiment scores:   8%|▊         | 512/6100 [00:47<06:52, 13.55it/s]Calculating sentiment scores:   8%|▊         | 514/6100 [00:47<06:40, 13.95it/s]Calculating sentiment scores:   8%|▊         | 516/6100 [00:47<06:47, 13.71it/s]Calculating sentiment scores:   8%|▊         | 518/6100 [00:47<06:19, 14.69it/s]Calculating sentiment scores:   9%|▊         | 520/6100 [00:47<05:53, 15.80it/s]Calculating sentiment scores:   9%|▊         | 522/6100 [00:48<05:53, 15.78it/s]Calculating sentiment scores:   9%|▊         | 524/6100 [00:48<06:21, 14.61it/s]Calculating sentiment scores:   9%|▊         | 526/6100 [00:48<06:49, 13.61it/s]Calculating sentiment scores:   9%|▊         | 528/6100 [00:48<07:04, 13.14it/s]Calculating sentiment scores:   9%|▊         | 530/6100 [00:48<08:46, 10.59it/s]Calculating sentiment scores:   9%|▊         | 532/6100 [00:48<08:07, 11.41it/s]Calculating sentiment scores:   9%|▉         | 534/6100 [00:49<07:57, 11.66it/s]Calculating sentiment scores:   9%|▉         | 536/6100 [00:49<08:45, 10.59it/s]Calculating sentiment scores:   9%|▉         | 538/6100 [00:49<08:16, 11.20it/s]Calculating sentiment scores:   9%|▉         | 540/6100 [00:49<08:03, 11.49it/s]Calculating sentiment scores:   9%|▉         | 542/6100 [00:49<07:25, 12.47it/s]Calculating sentiment scores:   9%|▉         | 544/6100 [00:50<08:36, 10.75it/s]Calculating sentiment scores:   9%|▉         | 546/6100 [00:50<08:29, 10.89it/s]Calculating sentiment scores:   9%|▉         | 548/6100 [00:50<09:38,  9.60it/s]Calculating sentiment scores:   9%|▉         | 550/6100 [00:50<08:38, 10.70it/s]Calculating sentiment scores:   9%|▉         | 552/6100 [00:50<07:55, 11.66it/s]Calculating sentiment scores:   9%|▉         | 554/6100 [00:50<07:00, 13.19it/s]Calculating sentiment scores:   9%|▉         | 556/6100 [00:51<07:14, 12.76it/s]Calculating sentiment scores:   9%|▉         | 558/6100 [00:51<06:37, 13.93it/s]Calculating sentiment scores:   9%|▉         | 560/6100 [00:51<06:47, 13.61it/s]Calculating sentiment scores:   9%|▉         | 562/6100 [00:51<07:12, 12.81it/s]Calculating sentiment scores:   9%|▉         | 564/6100 [00:51<06:47, 13.59it/s]Calculating sentiment scores:   9%|▉         | 566/6100 [00:51<08:51, 10.41it/s]Calculating sentiment scores:   9%|▉         | 568/6100 [00:52<08:06, 11.36it/s]Calculating sentiment scores:   9%|▉         | 570/6100 [00:52<08:44, 10.54it/s]Calculating sentiment scores:   9%|▉         | 572/6100 [00:52<11:08,  8.27it/s]Calculating sentiment scores:   9%|▉         | 573/6100 [00:52<12:52,  7.16it/s]Calculating sentiment scores:   9%|▉         | 574/6100 [00:53<13:28,  6.84it/s]Calculating sentiment scores:   9%|▉         | 576/6100 [00:53<11:00,  8.36it/s]Calculating sentiment scores:   9%|▉         | 578/6100 [00:53<11:48,  7.79it/s]Calculating sentiment scores:   9%|▉         | 579/6100 [00:53<11:24,  8.06it/s]Calculating sentiment scores:  10%|▉         | 581/6100 [00:53<09:46,  9.41it/s]Calculating sentiment scores:  10%|▉         | 583/6100 [00:53<08:53, 10.35it/s]Calculating sentiment scores:  10%|▉         | 585/6100 [00:54<08:09, 11.26it/s]Calculating sentiment scores:  10%|▉         | 587/6100 [00:54<07:50, 11.71it/s]Calculating sentiment scores:  10%|▉         | 589/6100 [00:54<07:35, 12.11it/s]Calculating sentiment scores:  10%|▉         | 591/6100 [00:54<07:06, 12.91it/s]Calculating sentiment scores:  10%|▉         | 593/6100 [00:54<07:17, 12.60it/s]Calculating sentiment scores:  10%|▉         | 595/6100 [00:54<07:17, 12.58it/s]Calculating sentiment scores:  10%|▉         | 597/6100 [00:55<08:29, 10.80it/s]Calculating sentiment scores:  10%|▉         | 599/6100 [00:55<09:32,  9.60it/s]Calculating sentiment scores:  10%|▉         | 601/6100 [00:55<11:32,  7.94it/s]Calculating sentiment scores:  10%|▉         | 603/6100 [00:55<10:25,  8.79it/s]Calculating sentiment scores:  10%|▉         | 605/6100 [00:55<09:01, 10.15it/s]Calculating sentiment scores:  10%|▉         | 607/6100 [00:56<09:13,  9.92it/s]Calculating sentiment scores:  10%|▉         | 609/6100 [00:56<08:50, 10.35it/s]Calculating sentiment scores:  10%|█         | 611/6100 [00:56<07:56, 11.52it/s]Calculating sentiment scores:  10%|█         | 613/6100 [00:56<08:04, 11.33it/s]Calculating sentiment scores:  10%|█         | 615/6100 [00:56<09:07, 10.02it/s]Calculating sentiment scores:  10%|█         | 617/6100 [00:57<09:35,  9.53it/s]Calculating sentiment scores:  10%|█         | 619/6100 [00:57<08:46, 10.42it/s]Calculating sentiment scores:  10%|█         | 621/6100 [00:57<07:39, 11.92it/s]Calculating sentiment scores:  10%|█         | 623/6100 [00:57<07:22, 12.38it/s]Calculating sentiment scores:  10%|█         | 625/6100 [00:57<08:21, 10.91it/s]Calculating sentiment scores:  10%|█         | 627/6100 [00:57<07:22, 12.35it/s]Calculating sentiment scores:  10%|█         | 629/6100 [00:58<06:41, 13.61it/s]Calculating sentiment scores:  10%|█         | 631/6100 [00:58<06:06, 14.91it/s]Calculating sentiment scores:  10%|█         | 634/6100 [00:58<05:33, 16.37it/s]Calculating sentiment scores:  10%|█         | 636/6100 [00:58<05:24, 16.84it/s]Calculating sentiment scores:  10%|█         | 638/6100 [00:58<05:27, 16.66it/s]Calculating sentiment scores:  10%|█         | 640/6100 [00:58<06:50, 13.29it/s]Calculating sentiment scores:  11%|█         | 642/6100 [00:58<06:39, 13.67it/s]Calculating sentiment scores:  11%|█         | 644/6100 [00:58<06:17, 14.45it/s]Calculating sentiment scores:  11%|█         | 646/6100 [00:59<06:32, 13.88it/s]Calculating sentiment scores:  11%|█         | 648/6100 [00:59<08:06, 11.21it/s]Calculating sentiment scores:  11%|█         | 650/6100 [00:59<07:47, 11.65it/s]Calculating sentiment scores:  11%|█         | 652/6100 [00:59<07:27, 12.17it/s]Calculating sentiment scores:  11%|█         | 654/6100 [00:59<07:18, 12.43it/s]Calculating sentiment scores:  11%|█         | 656/6100 [01:00<08:01, 11.30it/s]Calculating sentiment scores:  11%|█         | 658/6100 [01:00<07:27, 12.17it/s]Calculating sentiment scores:  11%|█         | 660/6100 [01:00<07:04, 12.82it/s]Calculating sentiment scores:  11%|█         | 662/6100 [01:00<06:37, 13.69it/s]Calculating sentiment scores:  11%|█         | 664/6100 [01:00<06:22, 14.19it/s]Calculating sentiment scores:  11%|█         | 666/6100 [01:00<06:23, 14.17it/s]Calculating sentiment scores:  11%|█         | 668/6100 [01:00<06:13, 14.53it/s]Calculating sentiment scores:  11%|█         | 670/6100 [01:01<06:12, 14.56it/s]Calculating sentiment scores:  11%|█         | 672/6100 [01:01<07:09, 12.65it/s]Calculating sentiment scores:  11%|█         | 674/6100 [01:01<08:07, 11.14it/s]Calculating sentiment scores:  11%|█         | 676/6100 [01:01<08:10, 11.05it/s]Calculating sentiment scores:  11%|█         | 678/6100 [01:01<07:41, 11.76it/s]Calculating sentiment scores:  11%|█         | 680/6100 [01:01<07:34, 11.92it/s]Calculating sentiment scores:  11%|█         | 682/6100 [01:02<06:43, 13.44it/s]Calculating sentiment scores:  11%|█         | 684/6100 [01:02<06:33, 13.76it/s]Calculating sentiment scores:  11%|█         | 686/6100 [01:02<07:41, 11.74it/s]Calculating sentiment scores:  11%|█▏        | 688/6100 [01:02<06:48, 13.26it/s]Calculating sentiment scores:  11%|█▏        | 690/6100 [01:02<07:24, 12.18it/s]Calculating sentiment scores:  11%|█▏        | 692/6100 [01:02<07:59, 11.28it/s]Calculating sentiment scores:  11%|█▏        | 694/6100 [01:03<07:10, 12.56it/s]Calculating sentiment scores:  11%|█▏        | 696/6100 [01:03<06:51, 13.12it/s]Calculating sentiment scores:  11%|█▏        | 698/6100 [01:03<07:16, 12.37it/s]Calculating sentiment scores:  11%|█▏        | 700/6100 [01:03<07:13, 12.46it/s]Calculating sentiment scores:  12%|█▏        | 702/6100 [01:03<06:44, 13.35it/s]Calculating sentiment scores:  12%|█▏        | 704/6100 [01:03<06:36, 13.60it/s]Calculating sentiment scores:  12%|█▏        | 706/6100 [01:03<06:00, 14.96it/s]Calculating sentiment scores:  12%|█▏        | 708/6100 [01:04<07:23, 12.15it/s]Calculating sentiment scores:  12%|█▏        | 710/6100 [01:04<07:15, 12.38it/s]Calculating sentiment scores:  12%|█▏        | 712/6100 [01:04<06:43, 13.34it/s]Calculating sentiment scores:  12%|█▏        | 714/6100 [01:04<07:52, 11.40it/s]Calculating sentiment scores:  12%|█▏        | 716/6100 [01:04<07:53, 11.37it/s]Calculating sentiment scores:  12%|█▏        | 718/6100 [01:05<10:36,  8.46it/s]Calculating sentiment scores:  12%|█▏        | 720/6100 [01:05<11:53,  7.55it/s]Calculating sentiment scores:  12%|█▏        | 721/6100 [01:05<12:50,  6.98it/s]Calculating sentiment scores:  12%|█▏        | 722/6100 [01:05<12:09,  7.38it/s]Calculating sentiment scores:  12%|█▏        | 723/6100 [01:05<12:23,  7.23it/s]Calculating sentiment scores:  12%|█▏        | 725/6100 [01:06<10:49,  8.28it/s]Calculating sentiment scores:  12%|█▏        | 726/6100 [01:06<12:48,  7.00it/s]Calculating sentiment scores:  12%|█▏        | 727/6100 [01:06<14:02,  6.38it/s]Calculating sentiment scores:  12%|█▏        | 728/6100 [01:06<13:48,  6.49it/s]Calculating sentiment scores:  12%|█▏        | 730/6100 [01:06<12:57,  6.90it/s]Calculating sentiment scores:  12%|█▏        | 731/6100 [01:07<12:35,  7.11it/s]Calculating sentiment scores:  12%|█▏        | 732/6100 [01:07<13:51,  6.45it/s]Calculating sentiment scores:  12%|█▏        | 733/6100 [01:07<13:23,  6.68it/s]Calculating sentiment scores:  12%|█▏        | 735/6100 [01:07<10:15,  8.71it/s]Calculating sentiment scores:  12%|█▏        | 737/6100 [01:07<08:45, 10.20it/s]Calculating sentiment scores:  12%|█▏        | 739/6100 [01:07<09:15,  9.65it/s]Calculating sentiment scores:  12%|█▏        | 741/6100 [01:08<09:06,  9.80it/s]Calculating sentiment scores:  12%|█▏        | 743/6100 [01:08<07:55, 11.27it/s]Calculating sentiment scores:  12%|█▏        | 745/6100 [01:08<09:20,  9.56it/s]Calculating sentiment scores:  12%|█▏        | 747/6100 [01:08<08:58,  9.94it/s]Calculating sentiment scores:  12%|█▏        | 749/6100 [01:08<09:40,  9.21it/s]Calculating sentiment scores:  12%|█▏        | 751/6100 [01:09<09:16,  9.61it/s]Calculating sentiment scores:  12%|█▏        | 753/6100 [01:09<10:59,  8.11it/s]Calculating sentiment scores:  12%|█▏        | 754/6100 [01:09<11:13,  7.93it/s]Calculating sentiment scores:  12%|█▏        | 756/6100 [01:09<10:24,  8.56it/s]Calculating sentiment scores:  12%|█▏        | 757/6100 [01:10<11:28,  7.77it/s]Calculating sentiment scores:  12%|█▏        | 759/6100 [01:10<11:07,  8.00it/s]Calculating sentiment scores:  12%|█▏        | 761/6100 [01:10<09:06,  9.76it/s]Calculating sentiment scores:  13%|█▎        | 763/6100 [01:10<08:11, 10.86it/s]Calculating sentiment scores:  13%|█▎        | 765/6100 [01:10<07:03, 12.59it/s]Calculating sentiment scores:  13%|█▎        | 767/6100 [01:10<06:25, 13.85it/s]Calculating sentiment scores:  13%|█▎        | 769/6100 [01:10<05:49, 15.24it/s]Calculating sentiment scores:  13%|█▎        | 771/6100 [01:10<05:39, 15.69it/s]Calculating sentiment scores:  13%|█▎        | 773/6100 [01:11<05:18, 16.75it/s]Calculating sentiment scores:  13%|█▎        | 775/6100 [01:11<05:19, 16.67it/s]Calculating sentiment scores:  13%|█▎        | 777/6100 [01:11<05:04, 17.51it/s]Calculating sentiment scores:  13%|█▎        | 779/6100 [01:11<05:06, 17.37it/s]Calculating sentiment scores:  13%|█▎        | 781/6100 [01:11<05:01, 17.62it/s]Calculating sentiment scores:  13%|█▎        | 784/6100 [01:11<04:49, 18.38it/s]Calculating sentiment scores:  13%|█▎        | 786/6100 [01:11<06:09, 14.39it/s]Calculating sentiment scores:  13%|█▎        | 789/6100 [01:12<06:07, 14.46it/s]Calculating sentiment scores:  13%|█▎        | 791/6100 [01:12<07:35, 11.66it/s]Calculating sentiment scores:  13%|█▎        | 793/6100 [01:12<06:46, 13.06it/s]Calculating sentiment scores:  13%|█▎        | 795/6100 [01:12<06:31, 13.54it/s]Calculating sentiment scores:  13%|█▎        | 797/6100 [01:12<07:01, 12.59it/s]Calculating sentiment scores:  13%|█▎        | 799/6100 [01:13<08:06, 10.90it/s]Calculating sentiment scores:  13%|█▎        | 801/6100 [01:13<07:11, 12.27it/s]Calculating sentiment scores:  13%|█▎        | 803/6100 [01:13<06:28, 13.65it/s]Calculating sentiment scores:  13%|█▎        | 805/6100 [01:13<06:46, 13.03it/s]Calculating sentiment scores:  13%|█▎        | 807/6100 [01:13<06:14, 14.13it/s]Calculating sentiment scores:  13%|█▎        | 809/6100 [01:13<06:07, 14.40it/s]Calculating sentiment scores:  13%|█▎        | 811/6100 [01:13<05:43, 15.40it/s]Calculating sentiment scores:  13%|█▎        | 813/6100 [01:13<05:23, 16.34it/s]Calculating sentiment scores:  13%|█▎        | 815/6100 [01:14<05:59, 14.71it/s]Calculating sentiment scores:  13%|█▎        | 817/6100 [01:14<05:37, 15.64it/s]Calculating sentiment scores:  13%|█▎        | 819/6100 [01:14<05:20, 16.47it/s]Calculating sentiment scores:  13%|█▎        | 821/6100 [01:14<05:09, 17.07it/s]Calculating sentiment scores:  13%|█▎        | 823/6100 [01:14<06:10, 14.23it/s]Calculating sentiment scores:  14%|█▎        | 825/6100 [01:14<06:54, 12.74it/s]Calculating sentiment scores:  14%|█▎        | 827/6100 [01:14<06:46, 12.97it/s]Calculating sentiment scores:  14%|█▎        | 829/6100 [01:15<08:00, 10.97it/s]Calculating sentiment scores:  14%|█▎        | 831/6100 [01:15<07:01, 12.51it/s]Calculating sentiment scores:  14%|█▎        | 833/6100 [01:15<06:22, 13.78it/s]Calculating sentiment scores:  14%|█▎        | 835/6100 [01:15<05:47, 15.15it/s]Calculating sentiment scores:  14%|█▎        | 837/6100 [01:15<05:33, 15.80it/s]Calculating sentiment scores:  14%|█▍        | 839/6100 [01:15<05:39, 15.50it/s]Calculating sentiment scores:  14%|█▍        | 841/6100 [01:15<05:55, 14.79it/s]Calculating sentiment scores:  14%|█▍        | 843/6100 [01:15<05:33, 15.75it/s]Calculating sentiment scores:  14%|█▍        | 845/6100 [01:16<05:35, 15.65it/s]Calculating sentiment scores:  14%|█▍        | 847/6100 [01:16<05:35, 15.66it/s]Calculating sentiment scores:  14%|█▍        | 849/6100 [01:16<06:06, 14.34it/s]Calculating sentiment scores:  14%|█▍        | 851/6100 [01:16<05:49, 15.03it/s]Calculating sentiment scores:  14%|█▍        | 853/6100 [01:16<06:41, 13.06it/s]Calculating sentiment scores:  14%|█▍        | 855/6100 [01:16<07:36, 11.48it/s]Calculating sentiment scores:  14%|█▍        | 857/6100 [01:17<07:38, 11.44it/s]Calculating sentiment scores:  14%|█▍        | 859/6100 [01:17<08:00, 10.91it/s]Calculating sentiment scores:  14%|█▍        | 861/6100 [01:17<07:21, 11.86it/s]Calculating sentiment scores:  14%|█▍        | 863/6100 [01:17<07:08, 12.23it/s]Calculating sentiment scores:  14%|█▍        | 865/6100 [01:17<08:35, 10.16it/s]Calculating sentiment scores:  14%|█▍        | 867/6100 [01:18<07:55, 11.00it/s]Calculating sentiment scores:  14%|█▍        | 869/6100 [01:18<07:42, 11.32it/s]Calculating sentiment scores:  14%|█▍        | 871/6100 [01:18<07:01, 12.40it/s]Calculating sentiment scores:  14%|█▍        | 873/6100 [01:18<07:59, 10.90it/s]Calculating sentiment scores:  14%|█▍        | 875/6100 [01:18<09:27,  9.21it/s]Calculating sentiment scores:  14%|█▍        | 877/6100 [01:19<09:14,  9.41it/s]Calculating sentiment scores:  14%|█▍        | 879/6100 [01:19<09:57,  8.74it/s]Calculating sentiment scores:  14%|█▍        | 881/6100 [01:19<08:48,  9.88it/s]Calculating sentiment scores:  14%|█▍        | 883/6100 [01:19<08:39, 10.03it/s]Calculating sentiment scores:  15%|█▍        | 885/6100 [01:19<09:10,  9.47it/s]Calculating sentiment scores:  15%|█▍        | 887/6100 [01:20<08:33, 10.15it/s]Calculating sentiment scores:  15%|█▍        | 889/6100 [01:20<07:45, 11.19it/s]Calculating sentiment scores:  15%|█▍        | 891/6100 [01:20<08:05, 10.74it/s]Calculating sentiment scores:  15%|█▍        | 893/6100 [01:20<08:39, 10.02it/s]Calculating sentiment scores:  15%|█▍        | 895/6100 [01:21<11:29,  7.55it/s]Calculating sentiment scores:  15%|█▍        | 897/6100 [01:21<09:55,  8.73it/s]Calculating sentiment scores:  15%|█▍        | 899/6100 [01:21<09:46,  8.87it/s]Calculating sentiment scores:  15%|█▍        | 900/6100 [01:21<10:53,  7.95it/s]Calculating sentiment scores:  15%|█▍        | 901/6100 [01:21<11:19,  7.65it/s]Calculating sentiment scores:  15%|█▍        | 902/6100 [01:21<11:54,  7.28it/s]Calculating sentiment scores:  15%|█▍        | 903/6100 [01:22<13:45,  6.29it/s]Calculating sentiment scores:  15%|█▍        | 905/6100 [01:22<13:13,  6.54it/s]Calculating sentiment scores:  15%|█▍        | 906/6100 [01:22<12:24,  6.97it/s]Calculating sentiment scores:  15%|█▍        | 908/6100 [01:22<10:02,  8.62it/s]Calculating sentiment scores:  15%|█▍        | 910/6100 [01:22<08:41,  9.96it/s]Calculating sentiment scores:  15%|█▍        | 912/6100 [01:22<08:09, 10.61it/s]Calculating sentiment scores:  15%|█▍        | 914/6100 [01:23<09:50,  8.78it/s]Calculating sentiment scores:  15%|█▌        | 916/6100 [01:23<10:01,  8.62it/s]Calculating sentiment scores:  15%|█▌        | 917/6100 [01:23<09:56,  8.69it/s]Calculating sentiment scores:  15%|█▌        | 919/6100 [01:23<10:30,  8.22it/s]Calculating sentiment scores:  15%|█▌        | 920/6100 [01:24<10:33,  8.18it/s]Calculating sentiment scores:  15%|█▌        | 921/6100 [01:24<11:42,  7.37it/s]Calculating sentiment scores:  15%|█▌        | 923/6100 [01:24<09:34,  9.02it/s]Calculating sentiment scores:  15%|█▌        | 925/6100 [01:24<08:27, 10.21it/s]Calculating sentiment scores:  15%|█▌        | 927/6100 [01:24<08:44,  9.86it/s]Calculating sentiment scores:  15%|█▌        | 929/6100 [01:24<09:14,  9.33it/s]Calculating sentiment scores:  15%|█▌        | 930/6100 [01:25<10:40,  8.07it/s]Calculating sentiment scores:  15%|█▌        | 931/6100 [01:25<10:31,  8.19it/s]Calculating sentiment scores:  15%|█▌        | 932/6100 [01:25<10:52,  7.92it/s]Calculating sentiment scores:  15%|█▌        | 934/6100 [01:25<08:59,  9.58it/s]Calculating sentiment scores:  15%|█▌        | 936/6100 [01:25<08:21, 10.29it/s]Calculating sentiment scores:  15%|█▌        | 938/6100 [01:25<07:43, 11.14it/s]Calculating sentiment scores:  15%|█▌        | 940/6100 [01:26<09:34,  8.98it/s]Calculating sentiment scores:  15%|█▌        | 942/6100 [01:26<08:52,  9.68it/s]Calculating sentiment scores:  15%|█▌        | 944/6100 [01:26<08:12, 10.46it/s]Calculating sentiment scores:  16%|█▌        | 946/6100 [01:26<08:22, 10.25it/s]Calculating sentiment scores:  16%|█▌        | 948/6100 [01:26<07:34, 11.34it/s]Calculating sentiment scores:  16%|█▌        | 950/6100 [01:27<07:21, 11.66it/s]Calculating sentiment scores:  16%|█▌        | 952/6100 [01:27<07:28, 11.49it/s]Calculating sentiment scores:  16%|█▌        | 954/6100 [01:27<08:14, 10.40it/s]Calculating sentiment scores:  16%|█▌        | 956/6100 [01:27<08:41,  9.87it/s]Calculating sentiment scores:  16%|█▌        | 958/6100 [01:27<08:04, 10.62it/s]Calculating sentiment scores:  16%|█▌        | 960/6100 [01:27<07:30, 11.40it/s]Calculating sentiment scores:  16%|█▌        | 962/6100 [01:28<07:00, 12.22it/s]Calculating sentiment scores:  16%|█▌        | 964/6100 [01:28<06:41, 12.79it/s]Calculating sentiment scores:  16%|█▌        | 966/6100 [01:28<06:24, 13.34it/s]Calculating sentiment scores:  16%|█▌        | 968/6100 [01:28<06:37, 12.90it/s]Calculating sentiment scores:  16%|█▌        | 970/6100 [01:28<06:22, 13.41it/s]Calculating sentiment scores:  16%|█▌        | 972/6100 [01:28<07:03, 12.11it/s]Calculating sentiment scores:  16%|█▌        | 974/6100 [01:29<06:51, 12.47it/s]Calculating sentiment scores:  16%|█▌        | 976/6100 [01:29<07:02, 12.12it/s]Calculating sentiment scores:  16%|█▌        | 978/6100 [01:29<07:10, 11.91it/s]Calculating sentiment scores:  16%|█▌        | 980/6100 [01:29<10:24,  8.19it/s]Calculating sentiment scores:  16%|█▌        | 981/6100 [01:29<11:14,  7.59it/s]Calculating sentiment scores:  16%|█▌        | 983/6100 [01:30<09:13,  9.24it/s]Calculating sentiment scores:  16%|█▌        | 985/6100 [01:30<09:36,  8.87it/s]Calculating sentiment scores:  16%|█▌        | 987/6100 [01:30<09:32,  8.92it/s]Calculating sentiment scores:  16%|█▌        | 989/6100 [01:30<09:22,  9.09it/s]Calculating sentiment scores:  16%|█▌        | 991/6100 [01:31<10:04,  8.45it/s]Calculating sentiment scores:  16%|█▋        | 992/6100 [01:31<10:56,  7.78it/s]Calculating sentiment scores:  16%|█▋        | 994/6100 [01:31<10:38,  7.99it/s]Calculating sentiment scores:  16%|█▋        | 995/6100 [01:31<11:18,  7.52it/s]Calculating sentiment scores:  16%|█▋        | 997/6100 [01:31<09:11,  9.26it/s]Calculating sentiment scores:  16%|█▋        | 998/6100 [01:31<09:09,  9.29it/s]Calculating sentiment scores:  16%|█▋        | 999/6100 [01:31<09:20,  9.10it/s]Calculating sentiment scores:  16%|█▋        | 1000/6100 [01:32<09:12,  9.23it/s]Calculating sentiment scores:  16%|█▋        | 1001/6100 [01:32<09:54,  8.57it/s]Calculating sentiment scores:  16%|█▋        | 1002/6100 [01:32<11:46,  7.21it/s]Calculating sentiment scores:  16%|█▋        | 1004/6100 [01:32<11:50,  7.17it/s]Calculating sentiment scores:  16%|█▋        | 1006/6100 [01:32<09:25,  9.01it/s]Calculating sentiment scores:  17%|█▋        | 1007/6100 [01:32<09:20,  9.09it/s]Calculating sentiment scores:  17%|█▋        | 1008/6100 [01:33<09:29,  8.95it/s]Calculating sentiment scores:  17%|█▋        | 1010/6100 [01:33<08:13, 10.32it/s]Calculating sentiment scores:  17%|█▋        | 1012/6100 [01:33<07:22, 11.49it/s]Calculating sentiment scores:  17%|█▋        | 1014/6100 [01:33<07:07, 11.90it/s]Calculating sentiment scores:  17%|█▋        | 1016/6100 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8.42it/s]Calculating sentiment scores:  17%|█▋        | 1042/6100 [01:36<08:47,  9.59it/s]Calculating sentiment scores:  17%|█▋        | 1044/6100 [01:36<07:43, 10.90it/s]Calculating sentiment scores:  17%|█▋        | 1046/6100 [01:36<07:13, 11.66it/s]Calculating sentiment scores:  17%|█▋        | 1048/6100 [01:37<07:14, 11.63it/s]Calculating sentiment scores:  17%|█▋        | 1050/6100 [01:37<07:20, 11.46it/s]Calculating sentiment scores:  17%|█▋        | 1052/6100 [01:37<07:15, 11.58it/s]Calculating sentiment scores:  17%|█▋        | 1054/6100 [01:37<06:57, 12.09it/s]Calculating sentiment scores:  17%|█▋        | 1056/6100 [01:37<06:31, 12.88it/s]Calculating sentiment scores:  17%|█▋        | 1058/6100 [01:37<06:34, 12.80it/s]Calculating sentiment scores:  17%|█▋        | 1060/6100 [01:37<06:13, 13.49it/s]Calculating sentiment scores:  17%|█▋        | 1062/6100 [01:38<06:11, 13.57it/s]Calculating sentiment scores:  17%|█▋        | 1064/6100 [01:38<05:51, 14.34it/s]Calculating sentiment scores:  17%|█▋        | 1066/6100 [01:38<05:39, 14.84it/s]Calculating sentiment scores:  18%|█▊        | 1068/6100 [01:38<05:44, 14.60it/s]Calculating sentiment scores:  18%|█▊        | 1070/6100 [01:38<05:49, 14.38it/s]Calculating sentiment scores:  18%|█▊        | 1072/6100 [01:38<06:21, 13.18it/s]Calculating sentiment scores:  18%|█▊        | 1074/6100 [01:38<06:34, 12.73it/s]Calculating sentiment scores:  18%|█▊        | 1076/6100 [01:39<06:47, 12.33it/s]Calculating sentiment scores:  18%|█▊        | 1078/6100 [01:39<07:51, 10.65it/s]Calculating sentiment scores:  18%|█▊        | 1080/6100 [01:39<07:06, 11.77it/s]Calculating sentiment scores:  18%|█▊        | 1082/6100 [01:39<06:56, 12.05it/s]Calculating sentiment scores:  18%|█▊        | 1084/6100 [01:39<06:48, 12.27it/s]Calculating sentiment scores:  18%|█▊        | 1086/6100 [01:40<08:02, 10.39it/s]Calculating sentiment scores:  18%|█▊        | 1088/6100 [01:40<07:12, 11.60it/s]Calculating sentiment scores:  18%|█▊        | 1090/6100 [01:40<06:56, 12.02it/s]Calculating sentiment scores:  18%|█▊        | 1092/6100 [01:40<06:55, 12.05it/s]Calculating sentiment scores:  18%|█▊        | 1094/6100 [01:40<07:02, 11.85it/s]Calculating sentiment scores:  18%|█▊        | 1096/6100 [01:40<06:33, 12.72it/s]Calculating sentiment scores:  18%|█▊        | 1098/6100 [01:40<06:19, 13.17it/s]Calculating sentiment scores:  18%|█▊        | 1100/6100 [01:41<05:54, 14.10it/s]Calculating sentiment scores:  18%|█▊        | 1102/6100 [01:41<07:07, 11.69it/s]Calculating sentiment scores:  18%|█▊        | 1104/6100 [01:41<06:30, 12.80it/s]Calculating sentiment scores:  18%|█▊        | 1106/6100 [01:41<06:12, 13.40it/s]Calculating sentiment scores:  18%|█▊        | 1108/6100 [01:41<05:42, 14.55it/s]Calculating sentiment scores:  18%|█▊        | 1110/6100 [01:41<05:27, 15.22it/s]Calculating sentiment scores:  18%|█▊        | 1112/6100 [01:41<05:24, 15.39it/s]Calculating sentiment scores:  18%|█▊        | 1114/6100 [01:42<05:35, 14.88it/s]Calculating sentiment scores:  18%|█▊        | 1116/6100 [01:42<05:45, 14.43it/s]Calculating sentiment scores:  18%|█▊        | 1118/6100 [01:42<05:24, 15.38it/s]Calculating sentiment scores:  18%|█▊        | 1120/6100 [01:42<05:37, 14.77it/s]Calculating sentiment scores:  18%|█▊        | 1122/6100 [01:42<05:38, 14.69it/s]Calculating sentiment scores:  18%|█▊        | 1124/6100 [01:42<05:31, 15.02it/s]Calculating sentiment scores:  18%|█▊        | 1126/6100 [01:42<06:02, 13.72it/s]Calculating sentiment scores:  18%|█▊        | 1128/6100 [01:43<06:26, 12.86it/s]Calculating sentiment scores:  19%|█▊        | 1130/6100 [01:43<08:55,  9.28it/s]Calculating sentiment scores:  19%|█▊        | 1132/6100 [01:43<08:19,  9.95it/s]Calculating sentiment scores:  19%|█▊        | 1134/6100 [01:43<09:09,  9.03it/s]Calculating sentiment scores:  19%|█▊        | 1136/6100 [01:44<08:27,  9.77it/s]Calculating sentiment scores:  19%|█▊        | 1138/6100 [01:44<08:20,  9.92it/s]Calculating sentiment scores:  19%|█▊        | 1140/6100 [01:44<08:15, 10.01it/s]Calculating sentiment scores:  19%|█▊        | 1142/6100 [01:44<08:25,  9.81it/s]Calculating sentiment scores:  19%|█▉        | 1144/6100 [01:44<09:11,  8.98it/s]Calculating sentiment scores:  19%|█▉        | 1145/6100 [01:45<09:31,  8.66it/s]Calculating sentiment scores:  19%|█▉        | 1146/6100 [01:45<09:18,  8.87it/s]Calculating sentiment scores:  19%|█▉        | 1148/6100 [01:45<08:30,  9.69it/s]Calculating sentiment scores:  19%|█▉        | 1150/6100 [01:45<07:49, 10.55it/s]Calculating sentiment scores:  19%|█▉        | 1152/6100 [01:45<06:46, 12.16it/s]Calculating sentiment scores:  19%|█▉        | 1154/6100 [01:45<06:36, 12.47it/s]Calculating sentiment scores:  19%|█▉        | 1156/6100 [01:45<07:02, 11.71it/s]Calculating sentiment scores:  19%|█▉        | 1158/6100 [01:46<06:55, 11.90it/s]Calculating sentiment scores:  19%|█▉        | 1160/6100 [01:46<06:13, 13.22it/s]Calculating sentiment scores:  19%|█▉        | 1162/6100 [01:46<06:40, 12.33it/s]Calculating sentiment scores:  19%|█▉        | 1164/6100 [01:46<06:35, 12.49it/s]Calculating sentiment scores:  19%|█▉        | 1166/6100 [01:46<06:11, 13.28it/s]Calculating sentiment scores:  19%|█▉        | 1168/6100 [01:46<06:08, 13.38it/s]Calculating sentiment scores:  19%|█▉        | 1170/6100 [01:47<06:19, 13.00it/s]Calculating sentiment scores:  19%|█▉        | 1172/6100 [01:47<05:56, 13.84it/s]Calculating sentiment scores:  19%|█▉        | 1174/6100 [01:47<06:08, 13.36it/s]Calculating sentiment scores:  19%|█▉        | 1176/6100 [01:47<06:00, 13.67it/s]Calculating sentiment scores:  19%|█▉        | 1178/6100 [01:47<07:39, 10.71it/s]Calculating sentiment scores:  19%|█▉        | 1180/6100 [01:47<07:03, 11.61it/s]Calculating sentiment scores:  19%|█▉        | 1182/6100 [01:47<06:37, 12.38it/s]Calculating sentiment scores:  19%|█▉        | 1184/6100 [01:48<06:27, 12.68it/s]Calculating sentiment scores:  19%|█▉    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14.30it/s]Calculating sentiment scores:  31%|███▏      | 1918/6100 [02:49<04:53, 14.24it/s]Calculating sentiment scores:  31%|███▏      | 1920/6100 [02:49<04:47, 14.53it/s]Calculating sentiment scores:  32%|███▏      | 1922/6100 [02:50<04:36, 15.12it/s]Calculating sentiment scores:  32%|███▏      | 1924/6100 [02:50<04:57, 14.05it/s]Calculating sentiment scores:  32%|███▏      | 1926/6100 [02:50<04:43, 14.70it/s]Calculating sentiment scores:  32%|███▏      | 1928/6100 [02:50<05:03, 13.73it/s]Calculating sentiment scores:  32%|███▏      | 1930/6100 [02:50<04:45, 14.59it/s]Calculating sentiment scores:  32%|███▏      | 1932/6100 [02:50<04:37, 15.03it/s]Calculating sentiment scores:  32%|███▏      | 1934/6100 [02:50<04:32, 15.31it/s]Calculating sentiment scores:  32%|███▏      | 1936/6100 [02:50<04:40, 14.83it/s]Calculating sentiment scores:  32%|███▏      | 1938/6100 [02:51<05:16, 13.14it/s]Calculating sentiment scores:  32%|███▏      | 1940/6100 [02:51<05:09, 13.45it/s]Calculating 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[02:55<06:29, 10.55it/s]Calculating sentiment scores:  33%|███▎      | 1992/6100 [02:55<06:34, 10.40it/s]Calculating sentiment scores:  33%|███▎      | 1994/6100 [02:55<06:09, 11.10it/s]Calculating sentiment scores:  33%|███▎      | 1996/6100 [02:55<06:03, 11.28it/s]Calculating sentiment scores:  33%|███▎      | 1998/6100 [02:55<05:31, 12.38it/s]Calculating sentiment scores:  33%|███▎      | 2000/6100 [02:55<05:21, 12.75it/s]Calculating sentiment scores:  33%|███▎      | 2002/6100 [02:56<05:32, 12.32it/s]Calculating sentiment scores:  33%|███▎      | 2004/6100 [02:56<05:21, 12.74it/s]Calculating sentiment scores:  33%|███▎      | 2006/6100 [02:56<05:12, 13.10it/s]Calculating sentiment scores:  33%|███▎      | 2008/6100 [02:56<05:12, 13.11it/s]Calculating sentiment scores:  33%|███▎      | 2010/6100 [02:56<04:58, 13.72it/s]Calculating sentiment scores:  33%|███▎      | 2012/6100 [02:56<04:44, 14.38it/s]Calculating sentiment scores:  33%|███▎      | 2014/6100 [02:56<04:33, 14.94it/s]Calculating sentiment scores:  33%|███▎      | 2016/6100 [02:57<04:46, 14.28it/s]Calculating sentiment scores:  33%|███▎      | 2018/6100 [02:57<04:49, 14.10it/s]Calculating sentiment scores:  33%|███▎      | 2020/6100 [02:57<05:28, 12.41it/s]Calculating sentiment scores:  33%|███▎      | 2022/6100 [02:57<06:38, 10.24it/s]Calculating sentiment scores:  33%|███▎      | 2024/6100 [02:57<06:28, 10.50it/s]Calculating sentiment scores:  33%|███▎      | 2026/6100 [02:58<06:51,  9.90it/s]Calculating sentiment scores:  33%|███▎      | 2028/6100 [02:58<06:15, 10.85it/s]Calculating sentiment scores:  33%|███▎      | 2030/6100 [02:58<05:33, 12.21it/s]Calculating sentiment scores:  33%|███▎      | 2032/6100 [02:58<06:15, 10.84it/s]Calculating sentiment scores:  33%|███▎      | 2034/6100 [02:58<06:03, 11.19it/s]Calculating sentiment scores:  33%|███▎      | 2036/6100 [02:58<05:54, 11.46it/s]Calculating sentiment scores:  33%|███▎      | 2038/6100 [02:58<05:11, 13.06it/s]Calculating 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9.61it/s]Calculating sentiment scores:  74%|███████▍  | 4539/6100 [06:33<03:10,  8.19it/s]Calculating sentiment scores:  74%|███████▍  | 4540/6100 [06:33<03:22,  7.70it/s]Calculating sentiment scores:  74%|███████▍  | 4541/6100 [06:34<03:48,  6.83it/s]Calculating sentiment scores:  74%|███████▍  | 4542/6100 [06:34<03:51,  6.74it/s]Calculating sentiment scores:  74%|███████▍  | 4543/6100 [06:34<03:37,  7.16it/s]Calculating sentiment scores:  75%|███████▍  | 4545/6100 [06:34<02:56,  8.82it/s]Calculating sentiment scores:  75%|███████▍  | 4547/6100 [06:34<02:38,  9.81it/s]Calculating sentiment scores:  75%|███████▍  | 4549/6100 [06:34<02:32, 10.20it/s]Calculating sentiment scores:  75%|███████▍  | 4551/6100 [06:34<02:13, 11.60it/s]Calculating sentiment scores:  75%|███████▍  | 4553/6100 [06:35<02:04, 12.38it/s]Calculating sentiment scores:  75%|███████▍  | 4555/6100 [06:35<02:20, 11.00it/s]Calculating sentiment scores:  75%|███████▍  | 4557/6100 [06:35<02:25, 10.59it/s]Calculating 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9.64it/s]Calculating sentiment scores:  78%|███████▊  | 4734/6100 [06:50<02:04, 10.98it/s]Calculating sentiment scores:  78%|███████▊  | 4736/6100 [06:50<02:05, 10.85it/s]Calculating sentiment scores:  78%|███████▊  | 4738/6100 [06:50<01:55, 11.78it/s]Calculating sentiment scores:  78%|███████▊  | 4740/6100 [06:50<01:55, 11.76it/s]Calculating sentiment scores:  78%|███████▊  | 4742/6100 [06:50<02:07, 10.68it/s]Calculating sentiment scores:  78%|███████▊  | 4744/6100 [06:51<02:07, 10.66it/s]Calculating sentiment scores:  78%|███████▊  | 4746/6100 [06:51<01:51, 12.11it/s]Calculating sentiment scores:  78%|███████▊  | 4748/6100 [06:51<02:07, 10.60it/s]Calculating sentiment scores:  78%|███████▊  | 4750/6100 [06:51<02:08, 10.49it/s]Calculating sentiment scores:  78%|███████▊  | 4752/6100 [06:51<02:08, 10.46it/s]Calculating sentiment scores:  78%|███████▊  | 4754/6100 [06:52<02:01, 11.04it/s]Calculating sentiment scores:  78%|███████▊  | 4756/6100 [06:52<02:14, 10.02it/s]Calculating 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9.25it/s]Calculating sentiment scores:  81%|████████  | 4921/6100 [07:07<01:49, 10.76it/s]Calculating sentiment scores:  81%|████████  | 4923/6100 [07:07<01:55, 10.17it/s]Calculating sentiment scores:  81%|████████  | 4925/6100 [07:08<01:52, 10.48it/s]Calculating sentiment scores:  81%|████████  | 4927/6100 [07:08<01:58,  9.90it/s]Calculating sentiment scores:  81%|████████  | 4929/6100 [07:08<01:45, 11.11it/s]Calculating sentiment scores:  81%|████████  | 4931/6100 [07:08<01:43, 11.30it/s]Calculating sentiment scores:  81%|████████  | 4933/6100 [07:08<01:58,  9.83it/s]Calculating sentiment scores:  81%|████████  | 4935/6100 [07:08<01:56, 10.01it/s]Calculating sentiment scores:  81%|████████  | 4937/6100 [07:09<02:13,  8.72it/s]Calculating sentiment scores:  81%|████████  | 4939/6100 [07:09<02:04,  9.36it/s]Calculating sentiment scores:  81%|████████  | 4940/6100 [07:09<02:19,  8.33it/s]Calculating sentiment scores:  81%|████████  | 4941/6100 [07:09<02:33,  7.55it/s]Calculating 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9.81it/s]Calculating sentiment scores:  85%|████████▌ | 5203/6100 [07:33<01:27, 10.30it/s]Calculating sentiment scores:  85%|████████▌ | 5205/6100 [07:33<01:17, 11.53it/s]Calculating sentiment scores:  85%|████████▌ | 5207/6100 [07:34<01:13, 12.19it/s]Calculating sentiment scores:  85%|████████▌ | 5209/6100 [07:34<01:06, 13.35it/s]Calculating sentiment scores:  85%|████████▌ | 5211/6100 [07:34<01:05, 13.60it/s]Calculating sentiment scores:  85%|████████▌ | 5213/6100 [07:34<01:06, 13.37it/s]Calculating sentiment scores:  85%|████████▌ | 5215/6100 [07:34<01:04, 13.67it/s]Calculating sentiment scores:  86%|████████▌ | 5217/6100 [07:34<01:03, 13.90it/s]Calculating sentiment scores:  86%|████████▌ | 5219/6100 [07:35<01:18, 11.16it/s]Calculating sentiment scores:  86%|████████▌ | 5221/6100 [07:35<01:12, 12.18it/s]Calculating sentiment scores:  86%|████████▌ | 5223/6100 [07:35<01:05, 13.48it/s]Calculating sentiment scores:  86%|████████▌ | 5225/6100 [07:35<01:08, 12.74it/s]Calculating 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Defining the Overall Rate

As a third step, I’ve defined a function named calculate_overall_rate to determine the overall sentiment rate for each comment based on the sentiment scores obtained from the ROBERTA model. The function identifies the maximum sentiment score among negative, neutral, and positive sentiments and assigns an overall rate of 1 for positive sentiment, -1 for negative sentiment, and 0 for neutral sentiment. I then apply this function to the sentiment scores calculated earlier for each Reddit comment to create a new column named Overall_Rate in the reddit data set.

Code

# Calculate Overall Rate for each comment
def calculate_overall_rate(scores):
    max_score = max(scores, key=scores.get)
    
    if max_score == 'roberta_pos':
        return 1
    elif max_score == 'roberta_neg':
        return -1
    else:
        return 0

reddit['Overall_Rate'] = [calculate_overall_rate(scores) for scores in sentiment_scores]

Distribution of Overall_Rate

Additionally, I want to show the distribution of the Overall_Rate it’s visualization across all the headphones.

Code

# Check distribution of Overall_Rate for each product
rate_distribution = reddit.groupby('Headphone Name')['Overall_Rate'].value_counts().unstack(fill_value=0)
rate_distribution

Overall_Rate	-1	0	1
Headphone Name
airpods pro 2nd generation	104	183	241
edifier stax spirit s3	44	98	62
jbl endurance peak ii	19	35	38
oneplus buds pro 2	94	207	116
samsung galaxy buds 2 pro	855	1214	818
samsung galaxy buds fe	61	109	104
sony wf-1000xm5	43	79	67
sony wh-1000xm5	119	295	278
sony wh-ch520	19	42	32
soundcore space q45	68	104	49
steelseries arctis nova pro	164	175	164

Code

# Check distribution of Overall_Rate for each product
rate_distribution = reddit.groupby('Headphone Name')['Overall_Rate'].value_counts().unstack(fill_value=0)

# Plotting the grouped bar plot for the distribution of Overall_Rate for each headphone
plt.figure(figsize=(20, 15))
rate_distribution.plot(kind='bar', figsize=(15, 8), colormap='viridis', edgecolor='k', linewidth=1.5)
plt.title('Distribution of Overall_Rate for Each Headphone', fontsize=20)
plt.xlabel('')
plt.ylabel('Count', fontsize=14)
plt.xticks(rotation=60, fontsize=16)
plt.legend(title='Overall_Rate', fontsize=20)
plt.tight_layout()
plt.show()

<Figure size 1920x1440 with 0 Axes>

From this table, and it’s visualization, I can see how many positive, neutral, negative comments each of the headphones have. These don’t tell that much about which headphones were the best/worst rated since all of them had different numbers of comments, but I will look into that further below.

Defining the Weighted Rate

As a forth step, I am firstly computing the mean of Overall_Rate for each headphone, named Mean_Rate. Then, I’m defining a Weighted_Rate variable, which is the mean Overall Rate multiplied with the number of positive/negative/neutral comments (depending whether the mean of Overall Rate for that specific headphone is positive/negative/neutral).

Code

# Grouping by 'Headphone Name' to calculate the required metrics
grouped_data = reddit.groupby('Headphone Name').agg(
        Number_of_Negative_Ratings=('Overall_Rate', lambda x: (x == -1).sum()),
        Number_of_Positive_Ratings=('Overall_Rate', lambda x: (x == 1).sum()),
        Mean_Rate=('Overall_Rate', 'mean')
    ).reset_index()

# Calculate the weighted mean based on the conditions
grouped_data['Weighted_Rate'] = grouped_data.apply(lambda row: 
                row['Mean_Rate'] * row['Number_of_Negative_Ratings'] 
                if row['Mean_Rate'] < 0 
                else (row['Mean_Rate'] * row['Number_of_Positive_Ratings'] 
                if row['Mean_Rate'] > 0 
                    else 0), axis=1)

# Sort the DataFrame by 'Weighted_Rate' in descending order
grouped_data = grouped_data.sort_values(by='Weighted_Rate', ascending=False).reset_index(drop=True)

# Displaying the new DataFrame
grouped_data

	Headphone Name	Number_of_Negative_Ratings	Number_of_Positive_Ratings	Mean_Rate	Weighted_Rate
0	sony wh-1000xm5	119	278	0.229769	63.875723
1	airpods pro 2nd generation	104	241	0.259470	62.532197
2	samsung galaxy buds fe	61	104	0.156934	16.321168
3	sony wf-1000xm5	43	67	0.126984	8.507937
4	jbl endurance peak ii	19	38	0.206522	7.847826
5	oneplus buds pro 2	94	116	0.052758	6.119904
6	edifier stax spirit s3	44	62	0.088235	5.470588
7	sony wh-ch520	19	32	0.139785	4.473118
8	steelseries arctis nova pro	164	164	0.000000	0.000000
9	soundcore space q45	68	49	-0.085973	-5.846154
10	samsung galaxy buds 2 pro	855	818	-0.012816	-10.957742

The reason why I chose this formula is because of different distributions of comment number across the headphones I have in my data set. If one headphone has the Mean_Rate negative, I would want to multiply it by the number of negative commets it had, and the same goes for the positive scenario. In the last table, I also saw that one of the headphones(steelseries arctis nova pro) had the same number of positive and negative comments, therefore the Mean_Rate of that headphone is 0(neutral).

Visualization of Weighted Rate

Now, I want to visualize the Weighted_Rate by using bar plot, with the headphones sorted based on their weighted rates in descending order.

Code

# Plotting the Weighted Mean Rate for each headphone
plt.figure(figsize=(12, 8))
sns.barplot(x='Weighted_Rate', y='Headphone Name', data=grouped_data.sort_values(by='Weighted_Rate', ascending=False), palette='viridis')
plt.title('Weighted Mean Rate by Headphone', fontsize=16)
plt.xlabel('Weighted Mean Rate', fontsize=14)
plt.ylabel('Headphone Name', fontsize=14)
plt.show()

From this visualization, I can see that only the Samsung Galaxy Buds 2 Pro and SoundCore Space q45 were rated as “not good” headphones from the reddit users/commenters. On the other hand, Sony wh-1000xm5 and AirPods Pro 2nd generation were rated the best from all these eleven headphones.

Comparing Results with Article Rate

As a last step, I want to see how well the weighted rate aligns with the article’s ratings for each headphone. Additionally, I want to also look at the correlations between the Weighted_Rate and Article Rate with the technical data from the headphones dataset, which highlights potential alignment or discrepancies between the two rating systems.

Code

# Merge the two DataFrames on 'Headphone Name'
merged_data = pd.merge(headphones, grouped_data, on='Headphone Name', how='inner')

# Sort the merged DataFrame based on 'Weighted_Rate' in descending order
merged_data = merged_data[['Headphone Name', 'Article Rate', 'Weighted_Rate']].sort_values(by='Weighted_Rate', ascending=False)

# Reset the index for better readability
merged_data.reset_index(drop=True, inplace=True)

# Displaying the ranked comparison
merged_data

	Headphone Name	Article Rate	Weighted_Rate
0	sony wh-1000xm5	1	63.875723
1	airpods pro 2nd generation	1	62.532197
2	samsung galaxy buds fe	0	16.321168
3	sony wf-1000xm5	1	8.507937
4	jbl endurance peak ii	0	7.847826
5	oneplus buds pro 2	0	6.119904
6	edifier stax spirit s3	1	5.470588
7	sony wh-ch520	1	4.473118
8	steelseries arctis nova pro	1	0.000000
9	soundcore space q45	1	-5.846154
10	samsung galaxy buds 2 pro	1	-10.957742

Correlation Map Comparison

Comparing the link between Weighted Rate with technical data vs the link between Article Rate with techical data:

Code

# Merge the datasets on 'Headphone Name'
merged_data_full = pd.merge(headphones, grouped_data, on='Headphone Name', how='inner')

# Calculate the correlation between 'Weighted_Rate' and each column in the headphones dataset
corr_weighted = merged_data_full.drop(['Headphone Name', 'Mean_Rate'], axis=1).corr()['Weighted_Rate'].sort_values(ascending=False)

# Calculate the correlation between 'Article Rate' and each column in the headphones dataset
corr_article = merged_data_full.drop(['Headphone Name', 'Mean_Rate'], axis=1).corr()['Article Rate'].sort_values(ascending=False)

# Combine the correlations into a single DataFrame for comparison
corr_comparison = pd.DataFrame({
    'Weighted_Rate': corr_weighted,
    'Article Rate': corr_article
}).reset_index()

# Melt the DataFrame to make it suitable for plotting
corr_comparison_melted = pd.melt(corr_comparison, id_vars=['index'], var_name='Rate Type', value_name='Correlation')

# Plotting the bar plot
plt.figure(figsize=(14, 10))
sns.barplot(x='index', y='Correlation', hue='Rate Type', data=corr_comparison_melted)
plt.title('Correlation Comparison between Weighted Rate and Article Rate', fontsize=16)
plt.xlabel('Feature', fontsize=14)
plt.ylabel('Correlation Coefficient', fontsize=14)
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

From this visualization, I can see that the Reddit comments/reviews don’t align with the website/articles ratings. A part of the reason is that articles may base their opinions on technical data, but the users on reddit share their experience with the products which in the end is more helpful to the ones who are looking to purchase those headphones. As for the correlation barplot visualization, I can see that there is a much weaker correlation between the technical data with Weighted Rate than with the Article Rate, which helps my reasoning that Article Rate may be based on technical data of the headphones.

Conclusion

In conclusion, in this project I wanted to analyze and compare user reviews on Reddit platform and online articles’ ratings for eleven specific headphones. The project constists of sentiment analysis using three models, topic modeling with LDA, and correlation analysis with technical specifications of the headphones. The Roberta model, which is trained on ~58M tweets, outperformed TextBlob and Vader in accurately assessing sentiment from Reddit comments, therefore I used this model to continue my setiment analysis, with extracting the Overall Rate for each comment. On the other hand, the headphones I chose, had different number of comments (some of them being more discussed on Reddit), therefore in order to perform a non-biased sentiment analysis I chose to create the Weighted_Rate variable. This rate represents the mean of the Overall Rate for each headphone, multiplied by the number of comments (positive/negative/neutral) each headphone had. With the help of LDA topic modelling, by looking at the general topics that were discussed on Reddit I understood the user preferences and concerns around the headphones. In the end, I observed that there was a weak correlation between Weighted_Rate from Reddit comments and technical specifications, indicating that user experiences may diverge from technical assessments. Moreover, there was a stronger correlation between Article Rate and technical data, suggesting that article ratings may be influenced by technical specifications. Overall, this project emphasizes the importance of considering both user experiences and technical specifications when evaluating headphones. While online articles may provide valuable technical insights, user reviews on platforms like Reddit offer authentic experiences that can guide potential customers in making informed decisions. On the other hand, accessing detailed technical information was quite challenging due to limited availability on brand websites therefore this project could be further enhanced if there was an easier access to technical data from official brand websites and by including a broader range of headphones for analysis. Such improvements would allow for a more detailed comparison between technical data and Reddit user comments.

References

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OnePlus Buds Pro 2. (n.d.). Retrieved from oneplus.com
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WF-1000XM5. (n.d.). Retrieved from sony.de
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